Application of Ensemble Classification Method for Power Transformers ...

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)

Application of Ensemble Classification Method for Power Transformers Condition Assessment Ayman Othman; Monsef Tahir; Ramadan El Shatshat; Khaled Shaban.

Abstract— The increase in the amount of data acquired from the monitoring of power system components has motivated utilities to employ effective strategies for processing the information collected. Hence, salient features can be identified and efficient decisions is made. An important component of any power system is power transformers, which have the single highest value of the equipment installed in high-voltage substations. For this reason, significant attention has been devoted to transformer monitoring and diagnostic techniques, resulting in huge volumes of raw data, especially related to the detection of any abnormal transformer behavior. The application of many monitoring tests is therefore not always useful, creating a critical need for a rational method of minimizing the number of monitoring tests without losing essential information about the actual condition of the transformer. This paper presents a statistical approach for evaluating the state of the transformer using machine learning technique. Demonstration of the use of classifier ensemble to predict transformer condition was also made. Index Terms—Transformer condition assessment, classification, ensemble classifier.

I. INTRODUCTION Power transformers are among the most expensive equipment of the electric power transmission and distribution system and their condition monitoring is important for the uninterrupted and reliable functioning of the power grid [1]. Compromising up to 60%, power transformers have the single highest value of the equipment installed in high-voltage substation [2]. Therefore, additional attention is currently being paid to life-cycle management and condition-monitoring techniques of transformers because of their important contribution in minimizing maintenance costs, and extending the nominal end of life. In a transformer, especially in-service older transformers, gradual deterioration occurs for a variety of reasons: overloading, lack of maintenance, design problems, environment temperature, and other factors that speed up the deterioration process and reduce life expectancy. Condition monitoring and assessment procedures are implemented with the goal of tracking component behavior and detecting early faults. As a result, maintenance programs can be improved, transformer failure rate can be decreased, direct and indirect costs due transformer outages can also be minimized, while overall system reliability and efficiency is enhanced. However,

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transformer monitoring techniques and maintenance procedures that produce a set of raw data for evaluating the health of the transformer are dependent on the experimental measurement practices employed and physical features of the asset. Approximately 27 assessment and monitoring methods have been reported in the literature [3], with a number of different techniques being introduced for diagnosing the condition of the transformer. An approach proposed in [4], [2], [5], [6] is based on a health index (HI) that incorporates the majority of thermal, mechanical, electrical, and chemical diagnostic tests. In [7], [8] chemical diagnostic methods and their interpretation schemes were reviewed. The use of techniques based on polarization measurements along with furan analysis for diagnosing the insulating system was presented in [9], [10]. In [11], a transformer state assessment based on the association rule of data and the variable weight synthesizing theory of factor space was proposed. High-performance liquid chromatography (HPLC) was used in these measurements in order to analyze cellulose aging. Other researchers have examined the thermal effect on insulating paper, as reported in [12], [13]. The investigation reported in [14] involved the use of a feature selection technique (FST) with a support vector machine (SVM) for identifying the most informative subset of oil characteristics for transformer condition assessment. The application of artificial intelligence for the estimation of transformers condition are widely investigated in literature based on the monitoring data [15], [16]. A multi-attribute decision-making evaluation model for transformer condition assessment has been introduced in [17]. In [18], artificial neural networks (ANN) and adaptive neuro-fuzzy inference system were proposed to determine the health index for power transformers, where technical and economical parameters are used as an input for the model. A support vector machine (SVM) algorithm has been developed in [19] to provide an intelligent tool for automatic measurement data analysis and transformer condition assessment prediction. SVM, neural networks, fuzzy logic, and particle swarm optimization have all been employed in order to translate test results into condition index, which have been applied to only a subset of monitoring techniques. Also, most of the monitoring techniques studied were capable of providing an assessment of the condition of the transformer, none addressed the number of tests required in order to determine the state of the transformer.

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)

This paper demonstrates the application of classifiers ensemble for assessing power transformer condition. Weak base learner was used in the ensemble classifier. Evaluation of produce model using threshold performance measures is used to inspect classifier model prediction exactness and completeness made in this paper. The organization of this paper is as follow, section 2 describes condition monitoring techniques. Section 3 presents ensemble condition assessment model and base discriminant classifier. Section 4 provides a case study to demonstrate the effectiveness of the proposed method compared to benchmark methods. Section 5 summarizes the results and section 6 finishes with conclusions. II. CONDITION MONITORING TECHNIQUES Utilities are paying increasing attention to the condition monitoring and assessment of power transformers, which are monitored through a variety of online and offline techniques. One benefit of power transformers condition monitoring is the creation of a historical measurement data record, which can be used to define the expected date and type of maintenance to perform, as well as assessing the condition of an individual transformer. The assessment of transformer condition usually involves monitoring of several parameters: partial discharge, hot spot temperature, moisture, oil quality, windings, etc. Dissolved gas analysis (DGA) [20], oil quality [21], furan tests [10], infra-red emission testing [22], [16], winding resistance [22], winding frequency response analysis tests [23], load tests [24], tap changer and bushing condition evaluations [25] are some of the tests and analyses that are most frequently conducted by utilities in order to assess the health of a transformer. The output obtained from the tests and monitoring mechanisms is a set of raw data, requiring tools or models for the processing of the information and the extraction of useful details or patterns about the condition of the transformer. III. ENSEMBLE CONDITION ASSESSMENT MODEL This paper applies ensemble classification method for power transformer condition assessment problem with weighted performance measures. The presence of sever class imbalance in the available multi-class test data would result in suboptimal prediction model, and hence, occurrence of misclassification for critical cases. The most sensitive cases are usually under represented and can be considered rare, hindering performance of classification methods. The use of single performance measure for this problem will result in optimistically biased prospective, for this, the application of different performance measure will be used in this work. A. Ensemble Classification Method Classification ensemble technique uses a group of classifiers to produce class predictions, achieving better generalization compared to single classifier and resulting in higher performance measures score. A group of base learners are trained on subsets of the training data to produce individual decisions which is later combined to produce final class prediction [26].

Depending on the nature of data and the kind of base learners used in the ensemble, different method can be used for ensemble generation. Class imbalance boosting based ensemble classifier focus on minority class samples through reducing majority class samples during training stages. Boosting methods adjusts class distribution by increasing minority class samples, as in Synthetic Minority OverSampling Technique (SMOTE) [27], [28], or by decreasing majority class samples, as in Random Under Sampling Boot (RUSTBoost) which uses random under-sampling for majority class [27]. B. Discriminant Analysis Classifier Discriminant classifiers are one of the nonparametric classification techniques, which relies on training class sample distributions to estimate the decision boundary parameters, where the decision boundary can be linear in sample feature space or in any other transformation of original space. Decision boundary parameters are found by formulating the problem as a minimization of a criterion function, like training error or sum of squared error. Discriminant function in a linear form can be written as [29], [30]: ( )=

+

(1)

Where is a weight vector and is threshold weight or bias. Samples of binary classifier are said to belong to class-1 if ( ) > 0, and belongs to class-2 if ( ) < 0 [29], [30]. Discriminant analysis can handle multi-class problems by reducing original problem to (C-1) binary class problems using one-versus-all approach, where C is the number of classes. Alternatively, reduction to C(C-1)/2 binary class problem can be considered using one-versus-one approach. Multiclass discriminant function can be written as: ( )=

+

, = 1, … ,

(2)

Where sample is assigned to class i if ( ) > ( ) for all ≠ . Generalized linear discriminant function is written as

( )= ∑

+

(3)

Where is component weight vector w. Equation 3 can be extended to quadratic form by adding terms constituting product of pairs. IV.

CASE STUDY

A. Dataset Description and Preprocessing Collecting a large set of data from many different tests without preprocessing makes the interpretation process very difficult. Specially with the presence of missing data points and some outliers. The used test data was provided by a utility company, consisting of 24 test results for 70 power transformers. Missing data points were filled with attribute mean and a moving average technique that replaces the outliers with the average of the neighbor data points was used. A ranking method was used to classify the measured data for each test from 4 to 1, where 4 is ranked as an excellent test result, 1 is ranked as a failure, and 2 and 3 represent in-between values.

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)

This ranking is determined based on consideration of the interpretations and limitations provided by national standards such as IEEE and ICE. The transformer conditions are divided into four classes: A, B, C and D, with A representing excellent condition or a new transformer, and D representing a poor or failed unit, as listed in Table I. The different Classes were distributed as follows: Class A: 24 % (17 units); class B: 47 % (33 units); class C: 21 % (15 units); class D: 10 % (7 units). TABLE I. TRANSFORMER C LASSES Class

Percentage %

Condition

A

85 % - 100 %

Excellent/New

B

70 % - 85 %

Very Good

C

50 % - 70 %

Maintain/Repair

D

30 % - 50 %

Poor

Exploratory data analysis of the test data using Principal Component Analysis (PCA) reveals within class scatter and inter-class separation as shown in Figure 1. The two eigenvectors corresponding to the highest variability in the dataset were used.

Figure 1. Dataset PCA projection

As an example, the classification of transformer conditions based on DGA was based on IEEE standard C57.104-1991 [20]. Table II shows sample of real data for the DGA gases of three 400 MVA / 245 KV transformers. The DGA results are ranked based on total dissolved combustible gases (TDCGA), with the highest mark being 4. If a TDCGA value < 720 ppm indicates excellent condition, a value between 721 ppm and 1920 ppm denotes good condition, between 1921 ppm and 4630 ppm designates a poor condition, and a TDCG > 4630 ppm signifies deteriorating condition. Data for DGA, infrared, oil quality, and furan tests for the sample transformers after ranking according to their classes are shown in Table III.

TABLE III. CLASSES AND RESULTS FROM FOUR TESTS FOR THREE UNITS

3.2

Infrared 3

Oil quality 3.2

3

3

2.8

2.6

2.9

2.7

Unit

DGA

1 2 3

Furan

Class

3.5

A

3

B

3

C

The methodology used for ranking transformer conditions for three, four, or five classes is still open to discussion; however, such a ranking or classification strategy has been proposed in a number of publications, for example, [31]. B. Classification Process The dataset was divided into training and testing datasets with 70% for training and 30% for testing. Ensemble classifiers with 4 weak learners, discriminant decision boundary in this case, was trained using 5-fold cross validation scheme. Trained model was later tested on the testing part. Stratified class distribution was used for the different folds to ensure classifier generalization during training process due to imbalance class distribution and the relatively small dataset size. The selection of 5-folds was specifically made to ensure representation of the different classes in all generated data subsets. Discriminant classifier with linear and quadratic decision boundaries coupled with RUSBoost ensemble were used in this work. C. Performance measures The selection of proper performance metrics to evaluate the system for bias is a crucial step when comparing different classifiers performance. This step is of higher importance when class imbalance is present which will be aggravated in the case of multi-class problem. The use of single performance measure in this condition can be misleading and will fail to predict performance on unseen data. For a more reliable assessment we will be using threshold metrics based on confusion matrix; namely accuracy, precision, recall, specificity, F-measure and balanced accuracy [27], [32]. These measures were calculated for each individual class and then weighted to produce a final index value. In this paper, the class of interest was considered as the positive class and the rest as the negative class. The metrics are defined as: =

(4)

=

(5)

(

)=

(6)

= −

(7) ∙

=

(8)

TABLE II. DGA TEST RANKING B ASED O N TDCG OF THREE UNITS H2

CH4

C2H6

C2H4

C2H2

CO

TDCG

Class

70

19

4

81

34

510

718

4

18

356

131

969

14

121

1609

3

203

879

316

1656

6

881

3941

2

=

(

)

+

(

)

(9)

Were TP, TN, FP and FN being true positive, true negative, false positive and false negative instances of

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)

classifier output respectively. Accuracy measures the ratio of correctly classified instances for positive and negative classes, which is very sensitive to class imbalance [27], [32]. Precision is a measure of positive classification exactness, number of true positive class instances in positive prediction. Recall is a measure of positive classification completeness, number of positive class instance classified correctly. Specificity is a measure of negative classification completeness, number of negative class instances correctly classified. F-measure is a combination of precision and recall in the form of weighted harmonic mean and can be adjusted to assign more weight to precision or recall. Balanced accuracy is the average of the percentage of correctly classified positive class samples and correctly classified negative class samples [27]. Finally, macro-averaged metrics were calculated using arithmetic mean of per class metric. V.

RESULTS AND PERFORMANCE MEASURES

three different ensemble kernels compared with SVM classifier. Average weighted metrics are used for the different classes after repeating the simulations for 10 times. Figure 2 presents classification balanced accuracy for different algorithms, while Figure 3 presents classification accuracy and Figure 4 shows classifiers F-measure for 10 iterations. It’s evident from the table and figures below the better performance of ensemble classifier over SVM classifier. The lower SVM performance is a direct result of the class imbalance which effects SVM separation boundaries due to the different class distribution densities around the separation hyper-plane. This results in a shifted separation boundary towards the minority class; and in turn increasing the number of falsepositive predictions and, hence, degrading the overall performance [27]. It worth noting that unsophisticated base learner with ensemble classification framework was comparable in performance to high performing multi-class LIBSVM library [33].

Results of ensemble discriminant classifier with four base learners are presented in Table IV. Reported results are for

TABLE IV. C LASSIFIER ENSEMBLE RESULTS (10-TIMES AVERAGE) Classifier

Kernel

Balanced Accuracy

Accuracy

Linear

0.8752

Discriminant

Diag-Linear

0.9374

Quadratic

0.5000

0.6719

Linear

0.9148

0.9524

RBF

0.5771

0.7329

Sigmoid

0.4968

0.6852

SVM

Figure 2. Different Kernels balanced accuracy comparative plot

Precision

Recall

0.8986

0.7912

0.8167

0.9336

0.7738

0.9547

0.9183

0.9061

0.9687

0.8924

0.0859

0.2500

0.7500

0.1278

0.9069

0.8639

0.9657

0.8642

0.3203

0.3554

0.7988

0.3238

0.0935

0.2458

0.7477

0.1353

F1-measure

Figure 4. Different Kernels F-measure comparative plot

VI.

Figure 3. Different Kernels weighted accuracy comparative plot

Specificity

CONCLUSION

This paper demonstrates the use of classifier ensemble for the assessment of power transformer condition, as well as the suitability of threshold performance measures to benchmark classifier models for this problem. The ensemble approach produces better performance on multi-class imbalanced data and has the advantage of generalizing more from the training dataset. The expected performance enhancement originates from the ability to generalize more from training data. To this end, slightly better if not comparable accuracy to SVM classifier with linear kernel was achieved using ensemble of four weak discriminant learners with modified Linear kernel.

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)

The use of balanced accuracy is more suited for prediction model assessment than overall accuracy, especially when considering other threshold metrics as precision, recall, specificity and F-measure. Applying the proposed technique facilitates more objective assessment of transformer condition through the generation of a more accurate models. VII.

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