GA trained parallel hidden layered ANN based differential protection of three phase power transformer

Balaga, Harish; Gupta, Neha; Vishwakarma, D. N.

doi:10.1016/j.ijepes.2014.11.028

Cited by 63 publications

(33 citation statements)

References 28 publications

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“…CT CT For the simulations necessary within this study, MATLAB was selected and especially the Power System Blockset included in Simulink [7]. Many advantages concluded in this selection, as for example the availability of models in Simulink, the fact that many users in the field utilize this tool in their work, the available literature and the well-documented user interface [8][9][10][11][12]. The simulation setup implemented is illustrated in figure 2.…”

Section: Differential Protection Relaymentioning

confidence: 99%

Differential Protection Schemes and Techniques for Power Transformers – Educational Aspects

Arapoglou¹,

Siderakis²

2016

Engineering and Industry

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A simulation model for the study of the differential protection in the case of power transformers has been developed. In this direction MATLAB Simulink has been employed and the operation during fault has been investigated, in the case of a two phase fault for different windings connections. The simulation results reveal the influence of the delta winding connection, which limits the asymmetrical loading experienced.

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Section: Differential Protection Relaymentioning

confidence: 99%

Differential Protection Schemes and Techniques for Power Transformers – Educational Aspects

Arapoglou¹,

Siderakis²

2016

Engineering and Industry

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“…The performance of an ANN-based DGA method had been compared with the Rogers ratios method, and it was found that the proposed ANN-based method detected more accurately. The BPNN model is the most popular one among various neural network algorithms and has been widely employed in different fields of fault diagnosis, such as power electronic system [34], transformer [35], battery [36,37], photovoltaic systems [38,39], etc. However, the BPNN model still has some intrinsic defects, for example, slow convergence speed and over-fitting problem [40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

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“…Because of the high dependence of transformer parameters on the magnetic curve and leakage inductance, inaccurate measurement of these is the main limitation of voltage and flux restraints, and inductance‐based approaches . Approaches in the literatures need a lot of training and complex computation in addition to being dependent on transformer parameters . In case of an internal fault with fault resistance or energization simultaneously with internal fault, these schemes have some restrictions that may influence the speed and security of the relay.…”

Section: Introductionmentioning

confidence: 99%

“…Among these are harmonic restraint, 1,2 voltage and flux restraints, 3,4 inductance-based method, 5,6 and digital signal processing. [7][8][9][10] The approaches based on harmonic restraint have limitations with new core steel improvement in modern transformers as inrush current may contain insufficient amount of second harmonics like internal faults. 11 Because of the high dependence of transformer parameters on the magnetic curve and leakage inductance, inaccurate measurement of these is the main limitation of voltage and flux restraints, and inductance-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…A number of approaches to distinguish inrush from internal fault currents have been reported in the literature. Among these are harmonic restraint, voltage and flux restraints, inductance‐based method, and digital signal processing . The approaches based on harmonic restraint have limitations with new core steel improvement in modern transformers as inrush current may contain insufficient amount of second harmonics like internal faults .…”

Section: Introductionmentioning

confidence: 99%

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Experimental results of ratios‐based transformer differential protection scheme

Ali

Malik

Abdelkader

et al. 2019

Int Trans Electr Energ Syst

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Summary Implementation of a recently published current and voltage ratios‐based differential protection algorithm proposed for transformers and results of experimental tests carried out on a physical transformer are presented and analyzed in this paper. The implementation is done using dSPACE DS1104 and MATLAB/Simulink software. Experimental studies of transient phenomena, including transformer energization, sympathetic inrush, energization with simultaneous internal fault, external faults under saturation of current transformer, and internal faults of the power transformer, show that the proposed scheme provides accurate, secure, and reliable protection for power transformers.

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GA trained parallel hidden layered ANN based differential protection of three phase power transformer

Cited by 63 publications

References 28 publications

Differential Protection Schemes and Techniques for Power Transformers – Educational Aspects

Differential Protection Schemes and Techniques for Power Transformers – Educational Aspects

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Experimental results of ratios‐based transformer differential protection scheme

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