Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.
In recent years, with the rapid development of China's economy, power system is developing towards the direction of ultrahigh voltage, large power grid, large capacity, and automation. Domestic demand for electricity has increased dramatically, and the national power industry is experiencing a rapid development stage. At present, the number of 110KV (66KV) and above voltage transformers transported by the State Grid Corporation has reached more than 30,000, with a total capacity of 3.4 TVA. Because the power transformer is in the central position of the power grid, the operation environment is complex, and, under the impact of various bad operating conditions, it is easy that fault occurs. Transformer faults has caused large area of breakage, resulting in a large number of economic losses. Therefore, the effective diagnosis of transformer faults is of great significance.
At present, the main testing and monitoring methods of transformer operation state are DC resistance measurement [
Due to the normal operation of transformer, transformer oil and solid insulation materials will gradually age and decompose into a small amount of gas. However, when the power equipment is faulted, especially in the case of overheating, discharge, or humidity, the amount of these gases will increase rapidly. It has been proved by long-term practice that the content of gas in oil is directly related to the faulty degree of transformer [
BP neural network is a kind of multilayer feedforward neural network, because of its simple structure, many adjustable parameters, many training algorithms, and good maneuverability. BP neural network has been widely used. According to statistics, 80%~90% of the neural network models are based on the BP network or its deformation [
In this paper, a diagnostic model which combines BP-Adaboost algorithm and PNN in series is proposed. Adaboost algorithm is a simple and easy algorithm, which can combine several weak classifiers to form a strong classifier. At the same time, the upper limit of the classification error rate will not increase with the overfitting of training. In addition, the Adaboost algorithm has the advantages of no need to adjust parameters and low generalization error rate. Because the Adaboost algorithm can construct multiple weak predictors with lower accuracy into a strong learner with higher accuracy, therefore, this paper combines BP neural network as a weak classifier with Adaboost algorithm. Considering that Adaboost algorithm is usually used to deal with binary classification problems, and transformer faults are often divided into many types of faults, this paper changes the multiclassification problem into multiple Adaboost binary classification problems to be solved. In the Adaboost algorithm, only the error rate of the weak classifier is slightly less than 1/2, but in the actual training process, there will still be special cases, which will affect the operation of the algorithm. In order to solve this problem, this paper revalues individual variables under special circumstances. Then the transformer fault is diagnosed by the improved BP-Adaboost algorithm. Samples that have not been successfully classified in the diagnosis results (A sample is divided into two or more different faults or not into any faults.) are reclassified as prediction samples, and the original training samples are reclassified as training samples and put into PNN neural network for diagnosis. The advantages of BP-Adaboost and PNN are fully combined by this series model. With samples which have not been successfully diagnosed by BP-Adaboost algorithm, the second diagnosis can be carried out by PNN, which effectively improves the accuracy of the model.
The main contributions of this paper are as follows.
(1) Firstly, the Adaboost algorithm is improved. It solves the defect that the diagnostic error of each weak classifier in the traditional Adaboost algorithm can only be within
(2) Then, BP-Adaboost multiclassification diagnosis algorithm is formed by combining the BP neural network as a weak classifier with multiclassification Adaboost algorithm. For samples diagnosed wrong in BP-Adaboost diagnosis model, they are put into PNN neural network for diagnosis again.
(3) Finally, the sample set is selected. Inspired by IEC three-ratio method, this paper not only takes five commonly used gas as characteristic parameters, but also takes C2H2/C2H4, CH4/H2, C2H4/C2H6 as characteristic parameters of transformer fault diagnosis.
Section
BP neural network is an error back propagation algorithm, which uses the steepest descent method to continuously adjust the weights and biases of the network are continuously adjusted to minimize the sum of square errors of the network. BP neural network consists of one input layer, one output layer, and several hidden layers. The training process is as follows.
(1) Establish BP neural network and initialize the weight and biases of BP neural network.
(2) Preprocess the sample data and set the number of neurons in each layer. Suppose
(3) Error
(4) If the errors produced do not meet the requirements, the steepest descent method is used to backpropagate the errors and adjust the weights and biases. Iterative cycle until the error meets the requirement.
Adaptive boosting (Adaboost) is a strong efficient algorithm that combines weak classifiers into strong classifiers. It was proposed by Yoav Freund and Robert Schapire in 1995. The main idea is as follows. Firstly, each training sample is given the same weight. Then the weak classifier is used to run iteratively T times; after each operation, the weight of training data is updated according to the classification results of training samples, and the wrong samples are usually given larger weight. For multiple weak classifiers, after running T times, a sequence of classification results of training samples is obtained. Each classification function is given a weight. The better the classification result, the greater the corresponding weight. The steps of the Adaboost algorithm are as follows.
Initialization data distribution weights
When the
Calculation of sequence weights
Formula (
Adjusting the weight of the next training sample according to the sequence weight,
After several weak classifiers are trained by T-rounds, the T-group weak classifier functions
BP-Adaboost algorithm flow.
Probabilistic neural network (PNN) is a parallel algorithm based on Bayes classification rules and Parzen window for probability density function estimation. PNN is a kind of artificial neural network with a simple structure, simple training, and wide application. Its structure consists of an input layer, mode layer, summation layer, and output layer. Its basic structure is shown in Figure
Basic structure of probabilistic neural network.
The values of training samples are first received through the input layer, and the number of neurons in the input layer is equal to the dimension of the input sample vector. Then the data information is transmitted to the pattern layer of the second layer through the input layer.
The pattern layer is used to calculate the matching relationship between input samples and each pattern in the training set. The number of neurons in the pattern layer is equal to the total number of training samples. Assuming that the vector of the input layer is
The summation layer is the accumulation of probabilities belonging to the same class, and its conditional probability density is
Generally, Adaboost combines with weak classifier to form a strong classifier to solve the problem of binary classification. But in transformer fault classification, there are not only two types of transformer faults. Therefore, BP-Adaboost needs to be transformed into multiclassification problems. In this paper, according to the total fault types to be classified, several BP-Adaboost two-classification models are established to classify each fault in turn. The specific classification operation is shown in Figure
BP-Adaboost multiclassification model.
In Figure
Multiple BP-Adaboost classification result.
Sample | a | b | c | d | e |
---|---|---|---|---|---|
BP-Adaboost classification result 1 | -1 | 1 | -1 | 1 | -1 |
BP-Adaboost classification result 2 | -1 | -1 | 1 | 1 | -1 |
BP-Adaboost classification result 3 | -1 | -1 | -1 | -1 | 1 |
Based on the possible error classification, the classification results of BP-Adaboost multiclassification model are set as follows. Firstly, the diagnosis type is coded and then the fault type is diagnosed according to the coding order. Then, each BP-Adaboost binary classification result is constructed into a
BP-Adaboost multiclassification results.
Sample | a | b | c | d | e |
---|---|---|---|---|---|
BP-Adaboost classification result 1 | -1 | 1 | -1 | 1 | -1 |
BP-Adaboost classification result 2 | -1 | -1 | 1 | 1 | -1 |
BP-Adaboost classification result 3 | -1 | -1 | -1 | -1 | 1 |
Classification result | 0 | 1 | 2 | 0 | 3 |
Usually we do not know the accuracy of the diagnostic results of the diagnostic model before comparing them with the real results. But for BP-Adaboost multiclassification diagnostic results in this paper, when “0” appears in the diagnostic results, it shows that the diagnosis must be wrong. In order to improve the accuracy of one algorithm, many scholars usually combine one algorithm with another to make the final experimental results better than any of them [
Diagnostic model of BP-Adaboost in tandem with PNN.
In transformer fault diagnosis, selecting representative data samples is more conducive to the establishment of a simulation model. Therefore, the basic principles of sample selection in this paper are as follows. (1) The fault samples selected are representative. (2) The selected samples should involve more complete fault type. (3) The samples should be compact. Therefore, this paper selected 100 representative samples from the historical faults of several oil-immersed power transformers in a 220V substation for empirical analysis. Types of transformer faults include medium and low temperature overheating, arc discharge, discharge and overheating fault, low energy discharge fault, and high temperature overheating fault.
For selection of training and test sets, in this paper, 20 samples were randomly selected as test data, and the remaining 80 samples were used as training data. In order to diagnose transformer faults accurately, the test samples are randomly selected according to the proportion of different types of samples. Specific fault codes and sample numbers are shown in Table
Sample data description.
The fault types | code | Total number of samples | Number of training samples | Number of testing samples |
---|---|---|---|---|
medium and low temperature overheating | 1 | 29 | 23 | 6 |
arc discharge | 2 | 12 | 10 | 2 |
discharge and overheating | 3 | 11 | 9 | 2 |
low energy discharge fault | 4 | 18 | 14 | 4 |
high temperature overheating | 5 | 30 | 24 | 6 |
Because of the difference of transformer internal faults, the gas produced by each fault is not completely the same. Principally, the fault related gases commonly used are hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). Since the components of H2, CH4, C2H6, C2H4, and C2H2 are closely related to the fault types of transformers, all the five gases are taken as the characteristic parameters of transformer fault diagnosis in this paper [
In the series model of BP-Adaboost and PNN, the number of BP neural networks with weak classifiers is 20. The training target of each BP neural network is 0.00004, the learning rate is 0.1, and the training time is 5. The selection of SPREAD in PNN neural network is 1.5. In the diagnosis of BP neural network, the training target is 0.01, the learning rate is 0.1, and the training time is 1000. In traditional genetic algorithm optimization of BP neural network (GA-BP), the population is 20 and the number of iterations is 50 (Testing environment: Core i5-3230M dual-core processor, running in the 2016a version of MATLAB).
Eight variables are selected as input vectors of the model, and different output vectors are set according to different BP-Adaboost binary classification models. According to Table
Because the test samples produced in each experiment are random, this paper tests the five models 10 times, and the test samples of the five models are the same as the training samples. The error results and the average running time of the four models tested 10 times are shown in Table
Number of samples per diagnostic error.
Number of diagnostic error samples | Average error rate | Average running time | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
BP-Adaboost | 4 | 4 | 2 | 6 | 4 | 2 | 5 | 2 | 4 | 4 | 18.5% | 10.495s |
BP | 7 | 7 | 9 | 8 | 9 | 6 | 8 | 7 | 9 | 9 | 39.5% | 0.6159s |
PNN | 5 | 5 | 8 | 5 | 9 | 5 | 7 | 1 | 6 | 5 | 28% | 0.1877s |
GA-BP | 2 | 0 | 3 | 4 | 4 | 1 | 3 | 3 | 3 | 2 | 12.5% | 723.52s |
BP-Adaboost_PNN | 1 | 3 | 1 | 4 | 4 | 0 | 2 | 2 | 2 | 2 | 10.5% | 11.253s |
From Table
In order to illustrate the validity of the model proposed in this paper, the results of one of the tests are taken as an example to analyze the effectiveness of the proposed model. Figures
Diagnosis result of BP neural network.
Diagnosis result of PNN neural network.
Diagnosis result of GA-BP neural network.
Equation (
Diagnostic results of BP-Adaboost and PNN tandem model.
In this paper, BP neural network and improved Adaboost are used to form a strong classifier. At the same time, several BP-Adaboost binary classifiers are established to form a multiclassifier. For the result matrix T formed by multiple binary classifiers, we can directly find some samples of classification errors and then put these samples of diagnosis errors into PNN neural network for rediagnosis. The reason why the proposed method can combine the diagnostic advantages of BP-Adaboost model and PNN model is that in BP-Adaboost multiclassification recognition, the classification accuracy of those samples which are only classified into one category is relatively high. Because it not only requires such samples to be classified into one type, but also ensures that the samples are not classified into other types. Under such stringent requirements, the classification accuracy of samples classified into only one category in BP-Adaboost is relatively high. Finally, the experimental results show that the diagnostic accuracy of the proposed series model in transformer fault diagnosis is significantly higher than that of BP neural network model, BP-Adaboost model, and PNN model. Although the accuracy is slightly better than that of GA-BP model, the diagnostic time of the proposed series model is obviously better than that of GA-BP model.
Power transformer plays an important role in power transmission and distribution. The performance of power transformer directly affects the operation of the whole power system. Therefore, it is very important to discover the faults of the transformer in advance. Whether early fault of the transformer can be eliminated as soon as possible is the key to ensure the stability of the power supply for users.
This paper presents a new diagnostic model of BP-Adaboost in series with PNN. By transforming BP-Adaboost biclassification model into a multiclassification model, on the basis of advantages of BP-Adaboost biclassification model, it also provides double guarantee for accurate classification samples of BP-Adaboost multiclassification model. For the type to be diagnosed, it is necessary to satisfy the need not to reclassify the classified samples into this type, but also to satisfy that the samples belonging to this type are diagnosed. Obviously, this is not always possible, which has a very high recognition accuracy for the two-class BP-Adaboost. Therefore, this paper transforms the result matrix T into vector
The data used to support the findings of this study are available from the corresponding author upon request.
There are no conflicts of interest regarding the publication of this paper.
This work was financially supported by the Project of National Natural Science Foundation of China (nos. 61502280, 61472228).