Fault detection in power transformers using random neural networks

Kaur, Amrinder; Brar, Yadwinder Singh; G, Leena

doi:10.11591/ijece.v9i1.pp78-84

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…A type of supervised learning is the BP algorithm [37], [38]. It takes place in two stages, "namely" forward propagation of information and backward propagation of errors [14].…”

Section: Back-propagation Algorithmmentioning

confidence: 99%

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Mas’ud

Jamal

Adewusi

et al. 2021

IJPEDS

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In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.

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“…A type of supervised learning is the BP algorithm [37], [38]. It takes place in two stages, "namely" forward propagation of information and backward propagation of errors [14].…”

Section: Back-propagation Algorithmmentioning

confidence: 99%

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Mas’ud

Jamal

Adewusi

et al. 2021

IJPEDS

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“…Three-phase voltages and six-line currents are used as input patterns for ANN. It can be used to detect and classify all line-toground fault types, which occurred in three phases of both transmission line circuits which resulting in breakdown the insulator [5][6][7][8][9][10]. The mutual effect problem that occurs in double circuit power system transmission lines causes inaccurate operation of the traditional protection device.…”

Section: Introductionmentioning

confidence: 99%

Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

Hussain¹,

Ali²,

Allu³

et al. 2020

IJPEDS

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This paper presents a developed logical tripping scheme to improve conventional protection performance. Adaptive single pole auto reclosure (ASPAR) system is proposed that considers, automatically tripping and reclosing of a multi-shot independent pole technique of a circuit breaker at a predetermined sequence, which can be used to boost the synchronization of the power grid under the transient fault conditions. Moreover, the ASPAR can be utilized to enhance the electrical system stability and reliability at the same operating conditions. Based on the three-phase system, the Artificial neural network (ANN) in this work has been done in order to diagnose and detect healthy and faulted phases. The proposed ANN fault classifier method consists of the logic gates, router circuits, timers, and positive and negative sequence analyses circuit. In addition, it is used to give the ability to recognize a fault type, which by training on the sequence angle values and coordination of the transmission line. Three-phase overhead transmission line including the proposed ASPAR is built in MATLA \SIMULINK environment. Thus the performance ANN-fault classified is tested under different fault conditions. Simulation results show that the proposed ASPAR based on ANN is accurate and well performance. Whereas resultant tripping and reclosing signals of ASPAR are successfully provided that enhances the circuit breaker mechanism under these operating condition.

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“…Transformer oil or insulating oil is a type of oil that is stable at high temperatures and has excellent electrical insulating properties [1]. The functions of transformer oil are to insulate, suppress corona discharge and arcing, and act as coolant [2,3]. The insulating oil properties should have high dielectric strength, thermal conductivity, chemical stability and able to handle high temperatures for extended periods.…”

Section: Introductionmentioning

confidence: 99%

Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

Mustafa

Ali

Zainuddin

et al. 2020

IJEECS

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Transformer is considered as one of the most important equipment in electrical power system networks. However, most problems occurred in transformer were related to the defects and weakness of the insulation systems. The oils used in transformer act as coolant and insulation purposes hence maintaining the dielectric strength of the transformer. In this work, electric field bridging pattern is observed from pre-breakdown and breakdown condition. The electric field bridging formation was recorded in the experimental setup and images were captured per frame. 193 images were randomly chosen from the whole video frames where 102 images were the pre-breakdown images and 91 images were the breakdown images. This system comprises of four stages: (i) a preprocessing stage to mark the electrodes tips and background subtraction; (ii) a segmentation stage to extract the electric field bridging formation in region of interest; (iii) a feature extraction stage to extract electric field bridging using feature descriptors, area, minor-axis and major-axis length (iv) a classification stage to identify the pre-breakdown and breakdown condition. System performance was evaluated using support vector machine (SVM), k-nearest neighbour (k-NN) and random forest (RF) and SVM provided the most promising accuracy that was 99%. The results show that the combination of three feature descriptors, area, minor-axis and major-axis length are the best features combination in identifying the transformer oil condition. In future work, further studies will be conducted to investigate the pattern of pre- and post-breakdown due to some similarity found in image pattern. Due to that, more feature descriptors will be identified to find a unique pattern between pre- and post-breakdown condition

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Fault detection in power transformers using random neural networks

Cited by 13 publications

References 11 publications

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

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