1,867 research outputs found
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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
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
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.Β Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD
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μ μννλ λμμ κ³ μ₯ λͺ¨λμ μ¬κ°λλ₯Ό μ§λ¨νλ λͺ¨λΈμ κ°λ°νμλ€.Due to the rapid development and advancement of todayβs 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 transformerβs 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 β with severity levels β 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
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Condition monitoring and diagnostics of power transformers
This paper addresses the subject of condition monitoring and diagnostics of power transformers. The main
results of two reliability surveys, carried out under the auspices of CIGRE and IEEE in order to assemble objective data
on the performance of transformers in service, are presented, providing useful information on the main causes of
transformer failures, the most likely affected components and the related outages times. A survey of the most important
methods, actually in use, for condition monitoring and diagnostics of power transformers is also given, which stresses
the need for the development of new diagnostic methods, that can be applied without taking the transformers out of
service, and that can also provide a fault severity criteria, in particular for determining transformers windings integrity.
Preliminary results, concerning the on-going research activity on the development of a new approach for inter-turn
winding fault diagnosis in three-phase transformers, are also reported in the paper
Development of nominal rules on the Fuzzy Sugeno method to determine the quality of power transformer insulation oil using Dissolved Gas Analysis data
This paper aims to develop the nominal rules on the Fuzzy Logic Method using the Sugeno-Fuzzy Inference System (FIS) for Dissolved Gas Analysis (DGA) and determine the quality of the power Transformer 1 and Transformer 6 insulating oil at the Buduran 150 kV substation. The nominal number of proposed fuzzy rules is 1920 rules. Implementing the Fuzzy-Sugeno method on Transformers 1 and 6 shows that the six input variables from the DGA test can produce a Total Dissolved Combustible Gas (TDCG) output value of 32.67 and 26.19 ppm, respectively. Both values indicate that the insulating oil of Transformers 1 and 6 are in condition one and, at the same time, indicates that the dissolved gas composition is in Normal status. Furthermore, the TDCG value, condition, and quality status of the insulating oil have the same or 100 % accuracy compared to the DGA test by PLN (UPT Surabaya). Thus, the nominal development of fuzzy rules using the Fuzzy-Sugeno method can perform DGA analysis more accurately to determine the quality of power transformer insulation oil compared to previous studies
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