Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing,”

Abstract. Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine

Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)

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

Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-levelHI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment

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

Online Monitoring Technical Basis and Analysis Framework for Large Power Transformers; Interim Report for FY 2012

The Light Water Reactor Sustainability program at Idaho National Laboratory (INL) is actively conducting research to develop and demonstrate online monitoring (OLM) capabilities for active components in existing Nuclear Power Plants. A pilot project is currently underway to apply OLM to Generator Step-Up Transformers (GSUs) and Emergency Diesel Generators (EDGs). INL and the Electric Power Research Institute (EPRI) are working jointly to implement the pilot project. The EPRI Fleet-Wide Prognostic and Health Management (FW-PHM) Software Suite will be used to implement monitoring in conjunction with utility partners: the Shearon Harris Nuclear Generating Station (owned by Duke Energy for GSUs, and Braidwood Generating Station (owned by Exelon Corporation) for EDGs. This report presents monitoring techniques, fault signatures, and diagnostic and prognostic models for GSUs. GSUs are main transformers that are directly connected to generators, stepping up the voltage from the generator output voltage to the highest transmission voltages for supplying electricity to the transmission grid. Technical experts from Shearon Harris are assisting INL and EPRI in identifying critical faults and defining fault signatures associated with each fault. The resulting diagnostic models will be implemented in the FW-PHM Software Suite and tested using data from Shearon-Harris. Parallel research on EDGs is being conducted, and will be reported in an interim report during the first quarter of fiscal year 2013

Characterization of Power Transformer Frequency Response Signature using Finite Element Analysis

Power transformers are a vital link in electrical transmission and distribution networks. Monitoring and diagnostic techniques are essential to decrease maintenance and improve the reliability of the equipment.This research has developed a novel, versatile, reliable and robust technique for modelling high frequency power transformers. The purpose of this modelling is to enable engineers to conduct sensitivity analyses of FRA in the course of evaluating mechanical defects of power transformer windings. The importance of this new development is that it can be applied successfully to industry transformers of real geometries