A combined ANN and expert system tool for transformer fault diagnosis

Wang, Z.; Liu, Y.; Griffin, P.J.

doi:10.1109/pesw.1999.747476

Cited by 40 publications

(47 citation statements)

References 11 publications

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“…In other words, it is essential to explore the principles, methods and means from various disciplines that are helpful to the fault diagnosis of transformers, 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, wavelet analysis (WA), optimized neural network, Bayesian network (BN), 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: Contentmentioning

confidence: 99%

“…For example, the EPS is considered one of the main forms of AI and the most active and extensive application fields in the application research of AI. Hence, in view of the professionalism, empiricism and complexity of transformer fault diagnosis, the application of EPS-based diagnosis methods has unique advantages [47][48][49][50][51]. Recently, several other approaches or techniques have been proposed for fault diagnosis of transformers, such as Rigatos and Siano's [82] proposed neural modeling and local statistical approach to fault diagnosis for the detection of incipient faults in power transformers, which can detect transformer failures at their early stages and consequently can deter critical conditions for the power grid; Shah and Bhalja [85] and Bacha et al [5] both proposed support vector machine (SVM)-based intelligent fault classification approaches to power transformer DGA.…”

Section: Contentmentioning

confidence: 99%

“…Moreover, these expert systems are integrated with a rich knowledge base which is developed based on fault phenomena, gas analysis in oil, and electrical and insulation testing results, as well as based on case diagnosis. In aspect of reasoning, these expert systems are combined with ANN [48], fuzzy mathematics [50], etc. and have shown the potential practical value and broad application prospect in practice [51].…”

Section: Description Of Eps-based Transformer Fault Diagnosis Using Dgamentioning

confidence: 99%

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

Cheng

2018

Energies

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Abstract:Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for 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 particular aspect, causing various degrees of shortcomings that cannot be resolved 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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Section: Contentmentioning

confidence: 99%

Section: Contentmentioning

confidence: 99%

Section: Description Of Eps-based Transformer Fault Diagnosis Using Dgamentioning

confidence: 99%

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

Cheng

2018

Energies

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“…Nowadays, the diagnostic methods that are most applied by maintenance engineering personnel, referred to here as classical diagnostic methods, are described at the standards IEC 60599 (IEC, 2007) Recently, most efforts in this area have been devoted to the development of the so-called intelligent or expert diagnostic systems and numerous papers have been published on this subject over the years (Lowe, 1985;Barrett, 1989;Tomsovic et al, 1993;Zhang et al, 1996;Wang and Liu, 1998;Huang, 2003;Fei and Zhang, 2009;Wu et al, 2011;Miranda et al, 2012). The expert systems try to improve the classical methods' performances, commonly by combining the responses of two or more of these methods and/or applying artificial intelligence techniques to empirical databases.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Thermal Decomposition of Mineral Insulating Oil

Cruz

Costa

Paredes

2015

Braz. J. Chem. Eng.

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-Dissolved gas analysis (DGA) has been applied for decades as the main predictive maintenance technique for diagnosing incipient faults in power transformers since the decomposition of the mineral insulating oil (MIO) produces gases that remain dissolved in the liquid phase. Nevertheless, the most known diagnostic methods are based on findings of simplified thermodynamic and compositional models for the thermal decomposition of MIO, in addition to empirical data. The simulation results obtained from these models do not satisfactorily reproduce the empirical data. This paper proposes a flexible thermodynamic model enhanced with a kinetic approach and selects, among four compositional models, the one offering the best performance for the simulation of thermal decomposition of MIO. The simulation results obtained from the proposed model showed better adequacy to reported data than the results obtained from the classical models. The proposed models may be applied in the development of a phenomenologically-based diagnostic method.

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“…The majority of them are techniques (Dukarm, 1993;Zhang et al, 1996;Yang and Huang, 1998;Wang et al, 1998;Guardado et al, 2001;Huang, 2003;Miranda and Castro, 2005;Naresh et al, 2008;Chen et al, 2009) built around a feed-forward neural-network classifier, that is also called Multi-Layer Perceptron (MLP) and that will be explained in the paper. Some of these papers introduce further enhancements to the MLP: in particular, neural networks that are run in parallel to an expert system in Wang et al (1998), Wavelet Networks (that is, neural nets with a wavelet-based feature extraction) in Chen et al (2009), Self-Organizing Polynomial Networks in Yang andHuang (1998) and Fuzzy Networks in Dukarm (1993), Huang (2003), Miranda and Castro (2005), and Naresh et al (2008). Several studies (Dukarm, 1993;Huang et al, 1997;Huang, 2003;Miranda and Castro, 2005;Naresh et al, 2008;Chen et al, 2009) resort to fuzzy logic (Huang, 2003) when modeling the decision functions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid diagnosing techniques for analyzing dissolved gases in power transformers

Vani¹,

Murthy²

2015

J. Eng. Technol. Res.

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Safe operation of elements of power systems plays a crucial role in maintaining the reliability and safety of the system. Transformers being a key element in power systems need to be maintained and monitored on a regular basis. Dissolved gas analysis has been used as a reliable tool in maintaining the safe operation of transformers for a long time. Analysis of dissolved gases is analytical and often interpreted differently by different users and methods. The scope of Artificial Intelligence tools in dissolved gas analysis has become critical with increasing number of transformers being used in power systems coupled with rapid expansion of transmission and distribution components. Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has emerged as one of the soft computing modeling technique for power transformer. An ANFIS model for dissolved gas analysis of power transformers is implemented. Similarly the GA-based weight optimization during training of an ANN is employed to improve diagnostic accuracy. A Graphical User Interface (GUI) is designed using Matlab to help in the seamless integration of analysis and decision making. The user interface is simple and easy to use providing the user flexibility and wide options for analysis. Traditional methods like Rogers Ratio, Key Gas Method, IEC Ratio Method, Dorenburg Ratio Method, Total Dissolved Combustible Gases Method and Triangle Method. The tools also incorporate fuzzy based analysis based on Rogers's ratios and Key Gas methods and analysis using Artificial Neural Networks. The primary motivation for the work is to provide a platform for analysis of dissolved gases to help in the early detection and diagnosis of transformer faults. This work is carried out with assistance from Andhra Pradesh State Transmission Corporation (APTRANSCO) in the form of required transformer analysis data and expert opinion for validation of the tool.

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A combined ANN and expert system tool for transformer fault diagnosis

Cited by 40 publications

References 11 publications

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

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

Simulation of Thermal Decomposition of Mineral Insulating Oil

Hybrid diagnosing techniques for analyzing dissolved gases in power transformers

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