Download Free Modelling And Condition Assessment Of Power Transformers Using Computational Intelligence Book in PDF and EPUB Free Download. You can read online Modelling And Condition Assessment Of Power Transformers Using Computational Intelligence and write the review.

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.
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.
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.
In modern industries, electrical energy conversion systems consist of two main parts: electrical machines and power electronic converters. With global electricity use at an all-time high, uninterrupted operation of electrical power converters is essential. Reliability in Power Electronics and Electrical Machines: Industrial Applications and Performance Models provides an in-depth analysis of reliability in electrical energy converters as well as strategies for designing dependable power electronic converters and electrical machines. Featuring a comprehensive discussion on the topics of reliability design and measurement, failure mechanisms, and specific issues pertaining to quality, efficiency, and durability, this timely reference source offers practical examples and research-based results for use by engineers, researchers, and advanced-level students.
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.
"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.
The book provides insights into International Conference on Smart Innovations in Communications and Computational Sciences (ICSICCS 2017) held at North West Group of Institutions, Punjab, India. It presents new advances and research results in the fields of computer and communication written by leading researchers, engineers and scientists in the domain of interest from around the world. The book includes research work in all the areas of smart innovation, systems and technologies, embedded knowledge and intelligence, innovation and sustainability, advance computing, networking and informatics. It also focuses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduce a need for a synergy of disciplines from science and technology. Sample areas include, but are not limited to smart hardware, software design, smart computing technologies, intelligent communications and networking, web and informatics and computational sciences.