Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network

Pan, Chong; Chen, W.; Yun, Yuxin

doi:10.1049/iet-epa:20070302

Cited by 48 publications

(27 citation statements)

References 12 publications

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“…Further application of hybrid genetic algorithm with other methods can be seen in numerous fields such as in yard crane scheduling problem (He et al, 2010), vehicle routing problem (Berger and Barkaoui, 2003), image segmentation problem (Awad et al, 2009), circuit ratiocut partitioning problem (Bui and Moon, 1998) and wavelet neural network problem (Pan et al, 2008).…”

Section: Hybrid Genetic Algorithm With Other Methodsmentioning

confidence: 99%

A Review on Automated Menu Planning Approaches

Ngo¹,

Cheah²,

Goh³

et al. 2016

Journal of Computer Science

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Abstract:Planning is widely been used in many areas such as in medicine, administration, business, logistics, education, environment and family matters. In automated planning research, the word "plans" refers specifically to plans of action. It is about the representation of future behavior, generally a series of action, with temporal and other constraints on them for execution by some agents. Theoretically, planning is an important part of rational behavior. In this study, we review some of the studies that have been conducted in menu planning. It covers the introduction of menu planning, the state of the arts for nutrition care expert systems and the approaches that have been applied. Furthermore, the advantages and limitations of the methods commonly used for menu planning are also highlighted in this study.

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Section: Hybrid Genetic Algorithm With Other Methodsmentioning

confidence: 99%

A Review on Automated Menu Planning Approaches

Ngo¹,

Cheah²,

Goh³

et al. 2016

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…To address this concern, various improved algorithms have been proposed, such as the BP neural network for variable learning rate [106], the homotopic BP algorithm [117], and the BP algorithm with momentum term [118]. Apart from the common BP neural network structure, there are some other types of network structure, such as probabilistic neural network structure [119], combined genetic algorithm (GA) multi-layer feedforward network [120], competitive learning theory based self-organized network [121], RBF network [122,123], and WNN [67,[124][125][126][127]. These improved ANN-based models have enhanced the accuracy of transformer fault diagnosis to varying degrees, which can be seen a new exploration of transformer fault diagnosis.…”

Section: Ann-based Transformer Fault Diagnosis Using Dga: a Surveymentioning

confidence: 99%

“…A summary of the application of ANN in DGA-based transformer fault diagnosis is presented in Table 6. [42,122,123,128,129] knowledge discovery-based neural network [43] knowledge extraction-based neural network [44] fuzzy reasoning-based neural network [45] MLP neural network-based decision [46] BP neural network [103] recurrent ANN [104] DL based ANN [105] hybrid ANN and EPS [106] GRNN [40,107] combined with mathematical morphology [108] combined GA multi-layer feedforward network [120,135] combined with competitive learning theory [121] WNN and FWNN [67,[124][125][126][127] EDA-ANN [131] combined with FAHP [134] …”

Section: Ann-based Transformer Fault Diagnosis Using Dga: a Surveymentioning

confidence: 99%

“…In addition to the five main categories of research approaches and techniques summarized above, there are some other intelligent algorithms, such as artificial immune algorithm (AIA) [72,163,176], GA [67,68,196], improved artificial fish swarm optimizer (IAFSO) [197][198][199], PSO [69,77,80], dynamic clustering (DC) [79,81], WA [83,[124][125][126][127], SVM [5,68,72,77,80,85,154,169,170,188,199], BN [87,[166][167][168], information fusion technology [200][201][202], extreme learning machine (ELM) [203][204][205], DL [70,71,105,206,207], optimized neural network [208,209], and evident...…”

Section: Application Of Other Intelligent Algorithms In Dga-based Tramentioning

confidence: 99%

“…Based on this, the flow chart of SGA is illustrated in Figure 8. According to the principle of GA shown in Figure 8, Pan et al [67] presented a fault diagnostic method based on a real-encoded hybrid GA evolving a wavelet neural network (WNN), which can be employed to optimize the structure and the parameters of WNN instead of humans in the same training process. This method overcomes some defects of a BP algorithm of WNN, the optimal procedure is easily stacked into the local minima and cases strictly demand initial value, for example, and can achieve a satisfactory compromise among network complexity, convergence and generalization ability.…”

Section: Application Of Si Algorithms In Transformer Fault Diagnosismentioning

confidence: 99%

See 2 more Smart Citations

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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Reliability‐based fault analysis models with industrial applications: A systematic literature review

Ahmed

Raza

Al-Anazi

2020

Quality & Reliability Eng

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Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets‐intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state‐of‐the‐art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer‐reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators. A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co‐citation network analysis. Later, a detailed content analysis of the top‐100 most‐cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability‐based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset‐intensive industry, researchers, and policymakers.

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Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network

Cited by 48 publications

References 12 publications

A Review on Automated Menu Planning Approaches

A Review on Automated Menu Planning Approaches

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

Reliability‐based fault analysis models with industrial applications: A systematic literature review

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