Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse

Colorado, D.; Hernández, J.A.; Rivera, W.; Martínez, H.; Juárez, D.

doi:10.1016/j.apenergy.2010.10.006

Cited by 27 publications

(8 citation statements)

References 28 publications

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“…The use of NH 3 -H 2 O is especially unsuitable for high temperature applications due to the very high pressures required (a temperature of 100 1C requires a pressure of $ 100 bar in the cycle) [71]. Thus while several heat transformer analyses have been conducted using this working fluid [80][81][82][83], it does not appear to match the favourable characteristics of LiBr-H 2 O.…”

Section: Ammonia-watermentioning

confidence: 96%

Recycling waste heat energy using vapour absorption heat transformers: A review

Donnellan

Cronin

Byrne

2015

Renewable and Sustainable Energy Reviews

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Section: Ammonia-watermentioning

confidence: 96%

Recycling waste heat energy using vapour absorption heat transformers: A review

Donnellan

Cronin

Byrne

2015

Renewable and Sustainable Energy Reviews

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“…With the rapid development of computer technology and artificial intelligence (AI) theory, machine learning-based techniques and data-driven modeling methods, including artificial neural network (ANN) [13][14][15][16][17], fuzzy theory [18,19], expert system (EPS) [20], rough sets theory (RST) [21], and other intelligent diagnosis methods [22][23][24][25][26][27][28][29][30][31] such as random forest (RF), gradient boosting decision tree (GBDT), deep belief network (DBN), support vector machine (SVM) and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiencies of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

et al. 2019

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The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

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“…In other words, it is essential to explore the principles, methods and means that are helpful to the fault diagnosis of transformer from various disciplines, so as to make the fault diagnosis technology interdisciplinary. Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37][38][39][40][41][42][43][44][45][46], expert system (EPS) [47][48][49][50][51], fuzzy theory [52][53][54][55][56][57][58], rough sets theory (RST) [36], grey system theory (GST) [59][60][61][62][63][64][65][66], and other intelligent diagnosis tools [5, such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, WA (wavelet analysis), optimized neural network, BN (Bayesian network), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiency of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”

Section: Fault Type Main Gas Component Minor Gas Componentmentioning

confidence: 99%

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

Cheng¹,

Chen²

2018

Preprint

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Compared with conventional methods in fault diagnosis of power transformers, which have defects such as imperfect encoding and too absolute encoding boundary, this paper systematically reveals various intelligent approaches applied in fault diagnosing and decision making of large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one certain aspect, causing some shortcomings in various degrees cannot be revealed effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse

Cited by 27 publications

References 28 publications

Recycling waste heat energy using vapour absorption heat transformers: A review

Recycling waste heat energy using vapour absorption heat transformers: A review

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

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