A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network

Mao, P.L.; Aggarwal, R.K.

doi:10.1109/mper.2001.4311488

Cited by 89 publications

(52 citation statements)

References 11 publications

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“…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%

“…Among them, mathematical statistics method [270], WA [83,[124][125][126][127], optimized neural network [208,209], BN [87,[166][167][168], and evidential reasoning approach [45,75,151,[210][211][212][213][214][215][216][217] have already appeared and have been preliminarily applied in the DGA-based fault diagnosis of the power transformers, and they are briefly reviewed as follows:…”

Section: Other Intelligent Diagnosis Toolsmentioning

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

confidence: 99%

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

confidence: 99%

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

confidence: 99%

Section: Other Intelligent Diagnosis Toolsmentioning

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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“…Wavelet transform combined with Artificial Neural Network (ANN) have been implemented for this purpose [11][12][13] . This paper proposes a new wavelet and ANN based three phase transformer protection algorithm to distinguish inrush currents from internal fault currents in three phase power transformers.…”

Section: Introductionmentioning

confidence: 99%

Wavelet and ANN Based Relaying for Power Transformer Protection

Sudha¹,

Jeyakumar²

2007

J. of Computer Science

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This paper presents an efficient wavelet and neural network (WNN) based algorithm for distinguishing magnetizing inrush currents from internal fault currents in three phase power transformers. The wavelet transform is applied first to decompose the current signals of the power transformer into a series of detailed wavelet components. The values of the detailed coefficients obtained can accurately discriminate between an internal fault and magnetizing inrush currents in power transformers. The detailed coefficients are further used to train an Artificial Neural Network (ANN). The trained ANN clearly distinguishes an internal fault current from magnetizing inrush current. A typical 750 MVA, 27/420KV, /Y power transformer connected between a 27KV source at the sending end and a 420KV transmission line connected to an infinite bus power system at the receiving end were simulated using PSCAD/EMTDC software. The generated data were used by the MATLAB software to test the performance of the proposed technique. The simulation results obtained show that the new algorithm is more reliable and accurate. It provides a high operating sensitivity for internal faults and remains stable for inrush currents of the power transformers.

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Diagnosis of interturn fault in stator winding of turbo‐generator

Faiz

Mahmoodi

Keravand

et al. 2019

Int Trans Electr Energ Syst

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Summary The synchronous generator is one of the most expensive and important components of power systems. Therefore, it must be protected against any fault. To prevent fault expansion, appropriate protection must be able to quickly diagnose generator internal faults. Meanwhile, protection system security depends on the stability of power system transients. One of the generator faults which may be undetectable by the existing advanced relays is the interturn fault. This paper introduces novel indices to detect this type of the fault which are based only on the measured current in the terminals of the generator. A quick algorithm can easily diagnose the fault. The merit of the algorithm is that it can detect the faults shorter than half of the cycle with correct operation. Furthermore, the proposed quick and secure approach has low computational burden and it can be simply applied to digital protection relays. To evaluate the performance of the proposed method, a high power synchronous generator is simulated using two‐dimensional finite element method. Also, its performance is verified for distinguishing interturn fault from power system transients and phase‐to‐phase faults.

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A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network

Cited by 89 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

Wavelet and ANN Based Relaying for Power Transformer Protection

Diagnosis of interturn fault in stator winding of turbo‐generator

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