Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers:A Novel Approach for Smart Grid-Ready Energy Management Systems

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning

Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder

With the mass construction of Gen III nuclear reactors, it is a popular trend to use deep learning (DL) techniques for fast and effective diagnosis of possible accidents. To overcome the common problems of previous work in diagnosing reactor accidents using deep learning theory, this paper proposes a diagnostic process that ensures robustness to noisy and crippled data and is interpretable. First, a novel Denoising Padded Autoencoder (DPAE) is proposed for representation extraction of monitoring data, with representation extractor still effective on disturbed data with signal-to-noise ratios up to 25.0 and monitoring data missing up to 40.0%. Secondly, a diagnostic framework using DPAE encoder for extraction of representations followed by shallow statistical learning algorithms is proposed, and such stepwise diagnostic approach is tested on disturbed datasets with 41.8% and 80.8% higher classification and regression task evaluation metrics, in comparison with the end-to-end diagnostic approaches. Finally, a hierarchical interpretation algorithm using SHAP and feature ablation is presented to analyze the importance of the input monitoring parameters and validate the effectiveness of the high importance parameters. The outcomes of this study provide a referential method for building robust and interpretable intelligent reactor anomaly diagnosis systems in scenarios with high safety requirements

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

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Power system security boundary visualization using intelligent techniques

In the open access environment, one of the challenges for utilities is that typical operating conditions tend to be much closer to security boundaries. Consequently, security levels for the transmission network must be accurately assessed and easily identified on-line by system operators;Security assessment through boundary visualization provides the operator with knowledge of system security levels in terms of easily monitorable pre-contingency operating parameters. The traditional boundary visualization approach results in a two-dimensional graph called a nomogram. However, an intensive labor involvement, inaccurate boundary representation, and little flexibility in integrating with the energy management system greatly restrict use of nomograms under competitive utility environment. Motivated by the new operating environment and based on the traditional nomogram development procedure, an automatic security boundary visualization methodology has been developed using neural networks with feature selection. This methodology provides a new security assessment tool for power system operations;The main steps for this methodology include data generation, feature selection, neural network training, and boundary visualization. In data generation, a systematic approach to data generation has been developed to generate high quality data. Several data analysis techniques have been used to analyze the data before neural network training. In feature selection, genetic algorithm based methods have been used to select the most predicative precontingency operating parameters. Following neural network training, a confidence interval calculation method to measure the neural network output reliability has been derived. Sensitivity analysis of the neural network output with respect to input parameters has also been derived. In boundary visualization, a composite security boundary visualization algorithm has been proposed to present accurate boundaries in two dimensional diagrams to operators for any type of security problem;This methodology has been applied to thermal overload, voltage instability problems for a sample system

Air-Gap Partial Discharge Development Stage Recognition for Power Transformer Insulation Monitoring Considering Effect of Cavity Size

Oil-paper insulation system is commonly used for power transformer internal insulation. Partial discharge (PD) is one of the main reasons for aging and disruption of the insulation system. Air-gap PD occurs in gas-filled cavity in transformer oil-paper insulation and is an extremely common and serious defect type. For air-gap PD analysis, most experiments were conducted through the standard air-gap discharge model recommended by CIGRE. Some work has been done to diagnose air-gap PD severity. However, the effect of cavity size on PD activity has not been emphasized yet. My thesis systematically discusses the effect of cavity size on air-gap PD activity through experiments. And pattern recognition classifier is a critical part in PD diagnosis. Artificial neural network and support vector machine are commonly used nowadays and show some good results in site application. To enhance PD diagnosis accuracy is still a main task. In this work, Random Forests is first time introduced in PD diagnosis. Experiments show that large cavity PD possesses lower inception field, higher charge magnitude, higher inception phase. PD happening in large cavity is more harmful than that happening in small cavity. Besides, during Air-gap PD development process, charge magnitude variation of large and small cavity model both presents concave curve shape with respect to time and discharge phase slowly expends. For small cavity model, when air-gap PD comes to the last stage, positive PD even can expand to the negative half cycle and vice versa. And through clustering, the PD development stage for large and small cavity model are both divided into three stages, initial discharge stage, weak discharge stage and pre-breakdown stage. For air-gap PD development stage identification, total accuracy of random forests classifier is 93.15%, showing a better performance than RBF neural network