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Author: Refat Atef Ghunem
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 57
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Book Description
"In this dissertation, artificial neural network is utilized as a modeling tool to predict transformer oil parameters. Accordingly, the diagnosis efficiency of several transformer condition monitoring techniques are enhanced and both corrective maintenance and end-of-life assessment costs are reduced. The research is focused in predicting parameters able to diagnose both transformer insulating oil and its paper insulation condition."--Abstract, p. iii.
Author: Refat Atef Ghunem
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 57
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Book Description
"In this dissertation, artificial neural network is utilized as a modeling tool to predict transformer oil parameters. Accordingly, the diagnosis efficiency of several transformer condition monitoring techniques are enhanced and both corrective maintenance and end-of-life assessment costs are reduced. The research is focused in predicting parameters able to diagnose both transformer insulating oil and its paper insulation condition."--Abstract, p. iii.
Author: W.H. Tang
Publisher: Springer Science & Business Media
ISBN: 0857290525
Category : Technology & Engineering
Languages : en
Pages : 210
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Book Description
In recent years, rapid changes and improvements have been witnessed in the field of transformer condition monitoring and assessment, especially with the advances in computational intelligence techniques. Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence applies a broad range of computational intelligence techniques to deal with practical transformer operation problems. The approaches introduced are presented in a concise and flowing manner, tackling complex transformer modelling problems and uncertainties occurring in transformer fault diagnosis. Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence covers both the fundamental theories and the most up-to-date research in this rapidly changing field. Many examples have been included that use real-world measurements and realistic operating scenarios of power transformers to fully illustrate the use of computational intelligence techniques for a variety of transformer modelling and fault diagnosis problems. Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence is a useful book for professional engineers and postgraduate students. It also provides a firm foundation for advanced undergraduate students in power engineering.
Author: Alhaytham Y. Al Qudsi
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 81
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Book Description
"Transformer Asset Management (TAM) is concerned with the strategic activities that monitor and manage the transformer asset in the power system. The outcomes of TAM aim at setting proper monitoring methods and maintenance plans, with minimal cost of time and money. Monitoring methods in the form of electrical, chemical and physical tests are conducted to assess the transformer operational condition. The main part, which is directly related to the ageing of the transformer, is the oil-paper insulation system. The standard practiced monitoring test methods used by TAM companies are considered highly effective and useful. However, a full feedback of the transformer’s condition requires a number of monitoring tests to be conducted. Such an exercise is considered expensive and difficult to implement for some of the tests. Moreover, the individual conducted tests cannot provide a comprehensive understanding of the transformer condition based on a single factor. Thus, the concept of the Health Index (HI) was developed to accurately assess the transformer’s condition and effective remnant age. The main components involved in the HI computation are related to the transformers' insulation condition, service record and design. Finding the transformer HI is normally done through using several industry computational methods. The drawback of these methods is the large number of tests required to achieve high level of condition assessment accuracy. Thus, alternative Artificially Intelligent (AI) methods should be used to design the HI model. AI methods, such as Artificial Neural Networks (ANN), can learn the pattern of the response output (HI), based on a given set of input (monitoring tests). The use of feature selection technique such as stepwise regression, can lead to an effective reduction of redundant tests in the presence of more significant ones. The presented work produces a general cost-effective AI based HI predictor model that can be used by different utility companies. Such a predictor would be able to produce a HI output value with a 95% prediction accuracy using only a subset of the required input features. Furthermore, the model can produce the same prediction accuracy with a predicted costly feature as one of the input features."--Abstract.
Author: Almas Shintemirov
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659357862
Category :
Languages : en
Pages : 308
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Book Description
Being one of the most expensive and important elements, a power transformer is a highly essential element, whose failures and damage may cause the outage of a power system. In practice, transformer condition assessment is mainly conducted by experts or trained on-site engineers based on a number of diagnostic techniques. In recent years, computational intelligence techniques have been widely utilized for advancing power transformer condition assessment methods. This book presents a number of novel intelligent techniques and approaches to deal with power transformer winding distortion and deformation assessment problem based on frequency response analysis and incipient faults classification problem in oil-filled power transformers based on dissolved gas analysis. Both theoretical introduction to the subject and practical examples using experimental measurements and simulation results are given. This book will benefit anyone associated with power transformer modelling and conditional assessment. It will also be useful for those working on applying computational intelligence to solving parameter identification and decision making problems in technical systems.
Author:
Publisher:
ISBN:
Category : Computational intelligence
Languages : en
Pages : 332
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Book Description
Examines the transformer operating health conditions due to ageing and incipient faults and evaluate its need for maintenance using computational intelligence tools to prevent premature failure and unplanned outages. Addressing the above challenge, the following the identified sub-problems; (i) Use of single factor data for transformer health assessment has led to unreliable evaluation in the past. Multi-factor data need to be applied for better transformer condition assessment. (ii) Application of simple multi-level computational intelligent tools that could yield better results for transformer condition-based maintenance assessment has been rare. (iii) The significance of each data source would be weighted differently as some sources present strong evidence than others. The weighting method adopted could affect the assessment results and this needs investigation. (iv) The entropy of formation for dissolved gases in transformer condition assessment has not been considered, it may help in weighting each dissolved gas and lead to accurate condition evaluation. (v) The sensitivity analysis of the data sources on the transformer condition assessment needs evaluation. This might help in identifying which inputs are critical and non-critical.
Author: Tapan Kumar Saha
Publisher: John Wiley & Sons
ISBN: 1119239966
Category : Science
Languages : en
Pages : 492
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Book Description
A one-stop guide to transformer ageing, presenting industrially relevant state-of-the-art diagnostic techniques backed by extensive research data Offers a comprehensive coverage of transformer ageing topics including insulation materials, condition monitoring and diagnostic techniques Features chapters on smart transformer monitoring frameworks, transformer life estimation and biodegradable oil Highlights industrially relevant techniques adopted in electricity utilities, backed by extensive research
Author: Hepu Deng
Publisher: Springer
ISBN: 3642242820
Category : Computers
Languages : en
Pages : 624
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Book Description
This book constitutes, together with LNAI 7002, LNAI 7003, and LNAI 7004, the refereed proceedings of the International Conference on Artificial Intelligence and ComputationaI Intelligence, AICI 2011, held in Taiyuan, China, in September 2011. The 265 revised full papers presented in the four volumes were carefully reviewed and selected from 1073 submissions. The 83 papers presented in this volume are organized in topical sections on applications of artificial intelligence; applications of computational intelligence; automated problem solving; brain models/cognitive science; data mining and knowledge discovering; expert and decision support systems; fuzzy logic and soft computing; intelligent agents and systems; intelligent control; intelligent image processing; intelligent scheduling; intelligent signal processing; natural language processing; nature computation; neural computation; pattern recognition; rough set theory.
Author: Zhenyuan Wang
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 210
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Book Description
This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution. Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented.
Author: Sivaji Chakravorti
Publisher: Springer Science & Business Media
ISBN: 1447155505
Category : Business & Economics
Languages : en
Pages : 289
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Book Description
Recent Trends in the Condition Monitoring of Transformers reflects the current interest in replacing traditional techniques used in power transformer condition monitoring with non-invasive measures such as polarization/depolarization current measurement, recovery voltage measurement, frequency domain spectroscopy and frequency response analysis. The book stresses the importance of scrutinizing the condition of transformer insulation which may fail under present day conditions of intensive use with the resulting degradation of dielectric properties causing functional failure of the transformer. The text shows the reader how to overcome the key challenges facing today’s maintenance policies, namely: The selection of appropriate techniques for dealing with each type of failure process accounting for the needs of plant owners, plant users and wider society; and Cost-efficiency and durability of effect. Many of the failure-management methods presented rely on the fact that most failures give warning when they are imminent. These potential failures give rise to identifiable physical conditions and the novel approaches described detect them so that action can be taken to avoid degeneration into full-blown functional failure. This “on-condition” maintenance means that equipment can be left in service as long as a specified set of performance standards continue to be met, avoiding the costly downtime imposed by routine and perhaps unnecessary maintenance but without risking equally expensive failure. Recent Trends in the Condition Monitoring of Transformers will be of considerable interest to both academic researchers in power systems and to engineers working in the power generation and distribution industry showing how new and more efficient methods of fault diagnosis and condition management can increase transformer efficiency and cut costs.
Author: Monsef Tahir
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
