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

정족수 감지 억제 적용에 따른 수처리용 분리막 생물반응기의 활성 슬러지와 생물막의 특성 변화

학위논문 (석사)-- 서울대학교 대학원 : 화학생물공학부, 2015. 2. 이정학.Recently, quorum sensing (QS) has been found to play a key role in biofilm formation on the membrane surface in membrane bioreactors (MBRs). Thus quorum quenching (QQ) which interrupts QS systems has received great attention as a fundamental solution to control biofouling in MBRs. Previous studies have proved that the bacterial intra-species QQ with lactonase producing bacteria, Rhodococcus sp. BH4, could efficiently alleviate the biofouling in MBRs. In addition to delay in transmembrane pressure (TMP) which is a fouling index, it has been also reported that QQ led to change in the production of extracellular polymeric substances (EPS) in biofilm. However, the analyses of previous studies were only performed considering the EPS in biofilm. In this study, we aimed to further characterize the effect of QQ considering both EPS in biofilm and mixed liquor while applying Rhodococcus sp. BH4 in two different types of MBRs. The first set of experiment was conducted with an anoxic/oxic combined MBR. In this set, soluble microbial product (SMP) was investigated in priority. In addition to examination of protein and polysaccharide, size exclusion chromatography (SEC) equipped with fluorescence detector was used to qualitatively analyze protein-like substances (Ex/Em wavelengths: 280/350 nm). Significant decrease in aromatic protein-like substances with molecular weight range of 100-1000 kDa was observed. Also, the filterability of sludge supernatant which depends on fouling tendency of SMP was evaluated by dead-end filtration with 150 kDa membrane. It was observed that QQ resulted in better filterability, shown by 2-3 times lower cake layer resistance. Another set of experiment with aerobic MBR was designed to study the QQ effect of Rhodococcus sp. BH4, considering not only SMP but also bound EPS of floc and biocake. Aromatic protein-like and humic acid-like substances (Ex/Em wavelengths: 280/350 nm and 345/443 nm, respectively) were analyzed with SEC. In case of EPS bound to floc, neither aromatic protein-like nor humic acid-like substances showed apparent difference. However, both components have decreased in biocake EPS, where humic acid-like substances were mostly removed by QQ.Chapter 1. Introduction 1 1.1. Background 2 1.2. Objectives 4 Chapter 2. Literature Review 5 2.1. Membrane Bioreactor (MBR) 6 2.1.1. Concept and Process 6 2.1.2. Development of MBR 8 2.2. Fouling Control in MBR Process 12 2.2.1. Physical Approach 12 2.2.2. Chemical Approach 12 2.2.3. Material Approach 14 2.2.4. Biological Approach 15 2.3. Quorum Sensing (QS) System 16 2.3.1. Definition and Mechanism 16 2.3.2. Gram-Negative Bacteria: LuxI/LuxR Type AI-1 QS 19 2.3.3. Gram-Positive Bacteria: Modified Oligopeptide Mediated AI-1 QS 23 2.3.4. Interspecies Communication: AI-2 QS 26 2.3.5. Role of QS in Biofilm Formation 29 2.3.6. Control Strategies of LuxI/LuxR Type AI-1 QS 30 2.4. Quorum Quenching (QQ) Application in MBR 36 2.4.1. Enzymatic QQ in MBR 36 2.4.2. Bacterial QQ in MBR 36 2.5. Extracellular Polymeric Substances (EPS) 38 2.5.1. Definition and Characteristics 38 2.5.2. EPS Analysis by Size Exclusion Chromatography (SEC) 39 2.5.3. EPS Analysis in MBR with QQ 40 Chapter 3. Materials and Methods 42 3.1. Preparation of QQ Agents 43 3.1.1. Strains and Growth Conditions 43 3.1.2. Preparation of Microbial Carriers 43 3.2. MBR Set-up 45 3.2.1. Anoxic/Oxic (A/O) MBR 45 3.2.2. Aerobic MBR 47 3.3. Measurement of QQ Activity 49 3.3.1. Substrate and Reporter Strain 49 3.3.2. QQ Activity of BH4 Vessels for A/O MBR 49 3.3.3. QQ Activity of W-beads for Aerobic MBR 50 3.4. Analytical Methods 51 3.4.1. A/O MBR 51 3.4.2. Aerobic MBR 56 Chapter 4. Results and Discussion 58 4.1. A/O MBR 59 4.1.1. QQ Activity of BH4 Vessel 59 4.1.2. General MBR Performances 61 4.1.3. Characterization of SMP 63 4.1.4. Filterability of Sludge Supernatant 67 4.2. Aerobic MBR 70 4.2.1. QQ Activity of BH4 W-bead 70 4.2.2. General MBR Performances 72 4.2.3. Characterization of Mixed Liquor EPS 74 4.2.4. Characterization of Biocake EPS 79 4.2.5. Comparison Between Mixed liquor and Biocake EPS 83 Chapter5. Conclusion 84 초록 87 Reference 89Maste