Multi-filter semi-supervised transformer model for fault diagnosis

Dissolved Gas Analysis (DGA) is the most commonly used method for power transformer fault diagnosis. However, very few reliable and labeled fault DGA samples are available in the transformer substation whilst DGA data without labels is easier to obtain, which makes it difficult to train fault detectors in high-dimensional input space or select features using wrapper methods. Therefore, in order to improve the fault diagnosis accuracy using limited labeled DGA samples but more unlabeled DGA data, this paper proposes a novel multi-filter semi-supervised feature selection method for selecting optimal DGA features and building effective fault diagnosis models. A confidence criterion is also proposed for selecting high confidence unlabeled data to expand the training data set. Five filter techniques based on different evaluation criteria are employed to rank input DGA features, and a feature combination method is then applied to aggregate feature ranks by multiple filters and form a lower-dimensional candidate feature subset. The proposed method has been tested by using the IEC T10 dataset and compared with traditional supervised diagnostic models. The results show that the proposed method works well in optimizing DGA features and improving fault diagnosis accuracy significantly. Besides, the robustness of the selection of optimal feature subset is validated by testing DGA samples from the local power utility

비표지 고장 데이터와 유중가스분석데이터를 이용한 딥러닝기반 주변압기 고장진단 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 소재웅.오늘날 산업의 급속한 발전과 고도화로 인해 안전하고 신뢰할 수 있는 전력 계통에 대한 수요는 더욱 중요해지고 있다. 따라서 실제 산업 현장에서는 주변압기의 안전한 작동을 위해 상태를 정확하게 진단할 수 있는 prognostics and health management (PHM)와 같은 기술이 필요하다. 주변압기 진단을 위해 개발된 다양한 방법 중 인공지능(AI) 기반 접근법은 산업과 학계에서 많은 관심을 받고 있다. 더욱이 방대한 데이터와 함께 높은 성능을 달성하는 딥 러닝 기술은 주변압기 고장 진단의 학자들에게 높은 관심을 갖게 해줬다. 그 이유는 딥 러닝 기술이 시스템의 도메인 지식을 깊이 이해할 필요 없이 대량의 데이터만 주어진다면 복잡한 시스템이라도 사용자의 목적에 맞게 그 해답을 찾을 수 있기 때문에 딥 러닝에 대한 관심은 주변압기 고장 진단 분야에서 특히 두드러졌다. 그러나, 이러한 뛰어난 진단 성능은 아직 실제 주변압기 산업에서는 많은 관심을 얻고 있지는 못한 것으로 알려졌다. 그 이유는 산업현장의 비표지데이터와 소량의 고장데이터 때문에 우수한 딥러닝기반의 고장 진단 모델들을 개발하기 어렵다. 따라서 본 학위논문에서는 주변압기 산업에서 현재 대두되고 있는 세가지 이슈를 연구하였다. 1) 건전성 평면 시각화 이슈, 2) 데이터 부족 이슈, 3) 심각도 이슈 들을 극복하기 위한 딥 러닝 기반 고장 진단 연구를 진행하였다. 소개된 세가지 이슈들을 개선하기 위해 본 학위논문은 세 가지 연구를 제안하였다. 첫 번째 연구는 보조 감지 작업이 있는 준지도 자동 인코더를 통해 건전성 평면을 제안하였다. 제안된 방법은 변압기 열하 특성을 시각화 할 수 있다. 또한, 준지도 접근법을 활용하기 때문에 방대한 비표지데이터 그리고 소수의 표지데이터만으로 구현될 수 있다. 제안방법은 주변압기 건전성을 건전성 평면과 함께 시각화하고, 매우 적은 소수의 레이블 데이터만으로 주변압기 고장을 진단한다. 두 번째 연구는 규칙 기반 Duval 방법을 AI 기반 deep neural network (DNN)과 융합(bridge)하는 새로운 프레임워크를 제안하였다. 이 방법은 룰기반의 Duval을 사용하여 비표지데이터를 수도 레이블링한다 (pseudo-labeling). 또한, AI 기반 DNN은 정규화 기술과 매개 변수 전이 학습을 적용하여 노이즈가 있는 pseudo-label 데이터를 학습하는데 사용된다. 개발된 기술은 방대한양의 비표지데이터를 룰기반으로 일차적으로 진단한 결과와 소수의 실제 고장데이터와 함께 학습데이터로 훈련하였을 때 기존의 진단 방법보다 획기적인 향상을 가능케 한다. 끝으로, 세 번째 연구는 고장 타입을 진단할 뿐만 아니라 심각도 또한 진단하는 기술을 제안하였다. 이때 두 상태의 레이블링된 고장 타입과 심각도 사이에는 불균일한 데이터 분포로 이루어져 있다. 그 이유는 심각도의 경우 레이블링이 항상 되어 있지만 고장 타입의 경우는 실제 주변압기로부터 고장 타입 데이터를 얻기가 매우 어렵기 때문이다. 따라서, 본 연구에서 세번째로 개발한 기술은 오늘날 데이터 생성에 매우 우수한 성능을 달성하고 있는 generative adversarial network (GAN)를 통해 불균형한 두 상태를 균일화 작업을 수행하는 동시에 고장 모드와 심각도를 진단하는 모델을 개발하였다.Due to the rapid development and advancement of today’s industry, the demand for safe and reliable power distribution and transmission lines is becoming more critical; thus, prognostics and health management (hereafter, PHM) is becoming more important in the power transformer industry. Among various methods developed for power transformer diagnosis, the artificial intelligence (AI) based approach has received considerable interest from academics. Specifically, deep learning technology, which offers excellent performance when used with vast amounts of data, is also rapidly gaining the spotlight in the academic field of transformer fault diagnosis. The interest in deep learning has been especially noticed in the field of fault diagnosis, because deep learning algorithms can be applied to complex systems that have large amounts of data, without the need for a deep understanding of the domain knowledge of the system. However, the outstanding performance of these diagnosis methods has not yet gained much attention in the power transformer PHM industry. The reason is that a large amount of unlabeled and a small amount of fault data always restrict their deep-learning-based diagnosis methods in the power transformer PHM industry. Therefore, in this dissertation research, deep-learning-based fault diagnosis methods are developed to overcome three issues that currently prevent this type of diagnosis in industrial power transformers: 1) the visualization of health feature space issue, 2) the insufficient data issue, and 3) the severity issue. To cope with these challenges, this thesis is composed of three research thrusts. The first research thrust develops a health feature space via a semi-supervised autoencoder with an auxiliary detection task. The proposed method can visualize a monotonic health trendability of the transformer’s degradation properties. Further, thanks to the use of a semi-supervised approach, the method is applicable to situations with a large amount of unlabeled and a small amount labeled data (a situation common in industrial datasets). Next, the second research thrust proposes a new framework, that bridges the rule-based Duval method with an AI-based deep neural network (BDD). In this method, the rule-based Duval method is utilized to pseudo-label a large amount of unlabeled data. Furthermore, the AI-based DNN is used to apply regularization techniques and parameter transfer learning to learn the noisy pseudo-labelled data. Finally, the third thrust not only identifies fault types but also indicates a severity level. However, the balance between labeled fault types and the severity level is imbalanced in real-world data. Therefore, in the proposed method, diagnosis of fault types – with severity levels – under imbalanced conditions is addressed by utilizing a generative adversarial network with an auxiliary classifier. The validity of the proposed methods is demonstrated by studying massive unlabeled dissolved gas analysis (DGA) data, provided by the Korea Electric Power Company (KEPCO), and sparse labeled data, provided by the IEC TC 10 database. Each developed method could be used in industrial fields that use power transformers to monitor the health feature space, consider severity level, and diagnose transformer faults under extremely insufficient labeled fault data.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 4 1.3 Dissertation Layout 7 Chapter 2 Literature Review 9 2.1 A Brief Overview of Rule-Based Fault Diagnosis 9 2.2 A Brief Overview of Conventional AI-Based Fault Diagnosis 11 Chapter 3 Extracting Health Feature Space via Semi-Supervised Autoencoder with an Auxiliary Task (SAAT) 13 3.1 Backgrounds of Semi-supervised autoencoder (SSAE) 15 3.1.1 Autoencoder: Unsupervised Feature Extraction 15 3.1.2 Softmax Classifier: Supervised Classification 17 3.1.3 Semi-supervised Autoencoder 18 3.2 Input DGA Data Preprocessing 20 3.3 SAAT-Based Fault Diagnosis Method 21 3.3.1 Roles of the Auxiliary Detection Task 23 3.3.2 Architecture of the Proposed SAAT 27 3.3.3 Health Feature Space Visualization 29 3.3.4 Overall Procedure of the Proposed SAAT-based Fault Diagnosis 30 3.4 Performance Evaluation of SAAT 31 3.4.1 Data Description and Implementation 31 3.4.2 An Outline of Four Comparative Studies and Quantitative Evaluation Metrics 33 3.4.3 Experimental Results and Discussion 36 3.5 Summary and Discussion 49 Chapter 4 Learning from Even a Weak Teacher: Bridging Rule-based Duval Weak Supervision and a Deep Neural Network (BDD) for Diagnosing Transformer 51 4.1 Backgrounds of BDD 53 4.1.1 Rule-based method: Duval Method 53 4.1.2 Deep learning Based Method: Deep Neural Network 54 4.1.3 Parameter Transfer 55 4.2 BDD Based Fault Diagnosis 56 4.2.1 Problem Statement 56 4.2.2 Framework of the Proposed BDD 57 4.2.3 Overall Procedure of BDD-based Fault Diagnosis 63 4.3 Performance Evaluation of the BDD 64 4.3.1 Description of Data and the DNN Architecture 64 4.3.2 Experimental Results and Discussion 66 4.4 Summary and Discussion 76 Chapter 5 Generative Adversarial Network with Embedding Severity DGA Level 79 5.1 Backgrounds of Generative Adversarial Network 81 5.2 GANES based Fault Diagnosis 82 5.2.1 Training Strategy of GANES 82 5.2.2 Overall procedure of GANES 87 5.3 Performance Evaluation of GANES 91 5.3.1 Description of Data 91 5.3.2 Outlines of Experiments 91 5.3.3 Preliminary Experimental Results of Various GANs 95 5.3.4 Experiments for the Effectiveness of Embedding Severity DGA Level 99 5.4 Summary and Discussion 105 Chapter 6 Conclusion 106 6.1 Contributions and Significance 106 6.2 Suggestions for Future Research 108 References 110 국문 초록 127박

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine

A new method of transformer fault diagnosis based on relevance vector machine (RVM) is proposed. Bayesian estimation is applied to support vector machine (SVM) in the novel algorithm, which made fault diagnosis system work more effectively. In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples. The process of transformer fault diagnosis for four working statuses is given in experiments and simulations. The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP) neural networks and general SVM methods

РОЗПІЗНАВАННЯ РОЗРЯДІВ, ЯКІ СУПРОВОДЖУЮТЬСЯ НИЗЬКОТЕМПЕРАТУРНИМИ ПЕРЕГРІВАМИ ЗА РЕЗУЛЬТАТАМИ АНАЛІЗУ РОЗЧИНЕНИХ У МАСЛІ ГАЗІВ ВИСОКОВОЛЬТНИХ ТРАНСФОРМАТОРІВ

Based on the analysis of test results for 135 high-voltage transformers, ranges of gas percentage, gas ratio values were obtained and nomograms for 10 types of combined defects were made, representing discharges with different intensity which are accompanied by overheating with temperature of 150-300°C. It has been established that in transformers with discharges accompanied by low-temperature overheating the values of CH4/H2, C2H2/CH4, C2H2/C2H6 and C2H2/C2H4 ratios determine the discharge energy, in accordance with the norms regulated by the most known standards, the C2H4/C2H6 ratio varies slightly depending on the hot spot temperature and the C2H6/CH4>1 ratio value. Dynamics of defects nomograms changing in the process of their development is analyzed. It is stated by the analysis results that in majority of cases the primary defect is discharges with different intensity, which are accompanied by low-temperature overheating. Overheating occurs in the process of discharge development. The analysis of recognition reliability of discharges with different intensity which are accompanied by 150-300°C overheating was made, using norms and criteria regulated by the most known standards and methods. The results of the analysis show that the most reliable recognition of the defects analyzed is provided to a large extent by the graphical methods, namely the ETRA square and the Duval triangle. The results obtained will significantly increase the recognition reliability of combined defects based on the results of the dissolved gas analysis in the oil.На основании анализа результатов испытаний по 135 высоковольтным трансформаторам получены диапазоны значений процентного содержания газов, значений отношений газов и построены номограмм для 10 типов комбинированных дефектов, представляющих собой разряды с разной степенью интенсивности которые сопровождаются нагревом с температурой 150-300°С. Установлено, что в трансформаторах с разрядами, которые сопровождаются низкотемпературными перегревами значения отношений: СН4/Н2, C2H2/CH4, C2H2/C2H6 и C2H2/C2H4 определяют энергию разрядов, в соответствии с нормами регламентируемыми в большинстве известных стандартов, значение отношения C2H4/C2H6 незначительно варьируется в зависимости от температуры горячей точки, а значение отношения C2H6/CH4>1. Проанализирована динамика изменения номограмм дефектов в процессе их развития. По результатам анализа установлено, что при развитии разрядов с разной степенью интенсивности, которые сопровождаются перегревами в диапазоне низких температур, в большинстве случаев первичным дефектом являются именно разряды. Перегревы возникают уже в процессе развития разрядов. Выполнен анализ достоверности распознавания разрядов с разной степенью интенсивности, которые сопровождаются нагревом с температурой 150-300°С, с использованием норм и критериев, регламентируемых наиболее известными стандартами и методиками. По результатам анализа установлено, что наибольшую достоверность распознавания, применительно к анализируемым дефектам обеспечивают в большей степени графические методы, а именно квадрат ЕТРА и треугольник Дюваля. Полученные результаты позволят существенно повысить достоверность распознавания комбинированных дефектов по результатам анализа растворенных в масле газов.На підставі аналізу результатів випробувань по 135 високовольтним трансформаторам отримані діапазони значень відсоткового вмісту газів, значення відношень газів і побудовані номограми для 10 типів комбінованих дефектів, що представляють собою розряди з різним ступенем інтенсивності, які супроводжуються перегрівами з температурою 150-300°С. Встановлено, що в трансформаторах з розрядами, які супроводжуються низькотемпературними перегрівами, значення відношень: СН4/Н2, C2H2/CH4, C2H2/C2H6 і C2H2/C2H4 визначають енергію розрядів, відповідно до норм, що регламентуються у більшості відомих стандартів, значення відношення C2H4/C2H6 незначно варіюється залежно від температури «гарячої точки», а значення відношення C2H6/CH4>1. Проаналізовано динаміку зміни номограм дефектів у процесі їх розвитку. За результатами аналізу встановлено, що при розвитку розрядів з різним ступенем інтенсивності, які супроводжуються перегрівами в діапазоні низьких температур, у більшості випадків первинним дефектом є саме розряди. Перегріви виникають вже в процесі розвитку розрядів. Виконано аналіз достовірності розпізнавання розрядів з різним ступенем інтенсивності, які супроводжуються перегрівами з температурою 150-300°С, з використанням норм і критеріїв, регламентованих найбільш відомими стандартами і методиками. За результатами аналізу встановлено, що найбільшу достовірність розпізнавання, стосовно аналізованих дефектів забезпечують більшою мірою графічні методи, а саме квадрат ЕТРА і трикутник Дюваля. Отримані результати дозволять істотно підвищити достовірність розпізнавання комбінованих дефектів за результатами аналізу розчинених у маслі газів

Fault diagnosis of transformer using association rule mining and knowledge base

Association rule mining makes interesting associations and/or correlations among large sets of data. Those associations can be refined as decision rules to be used and stored in a knowledge base system. In this paper, an approach based on association rule and knowledge base is proposed and implemented in the fault diagnosis of a transformer system. According to the features of association rule, the Apriori algorithm is adopted and modified to generate decision rules from power transformer information for building knowledge base, then the rules can be refined to diagnose the fault of the transformer through reasoning, and a prototype system is developed. This approach based on association rule is described in detail and the application is illustrated by an example. A comparison with the IEC (International Electrotechnical Commission) three-ratio method shows the proposed method can provide better accuracy in performance. © 2010 IEEE

Fault detection in power transformers using random neural networks

This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained  that BFGS has better performance for the diagnosis of fault in transformer as compared to LM

Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD