Multiple incipient fault classification approach for enhancing the accuracy of dissolved gas analysis (DGA)

Wani, Shufali Ashraf; Gupta, Dhawal; Farooque, Md. Umar; Khan, Shakeb A.

doi:10.1049/iet-smt.2018.5135

Cited by 41 publications

(20 citation statements)

References 23 publications

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“…1 shows the three stages of failure detection that are used in the design of the FDI method. This study seeks to solve the problems presented by current fault diagnosis systems, which many times provide an insufficient opinion that serves as a basis for taking timely actions that enable the efficient management of preventive maintenance [25], [26] and the inconveniences in the Identification of multiple failures [27]. The bibliography consulted shows the use of multiple failure classification tools [26], where the use of the interpretation guidelines for Key Gases and the methods of Doernenburg, Rogers, IEC and Duval [3] is generalized.…”

Section: Design Of the Fault Diagnosis Systemmentioning

confidence: 99%

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

Fernández¹,

Corrales-Barrios

Benítez-Pina

et al. 2022

IREE

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The availability of power transformers is essential for the safety and continuity of electrical service. Today's fault diagnosis methods use intelligent techniques such as neural networks, support machines, hybrid techniques, among others. Although they present good results, these techniques find restrictions in the ability to determine the precise moment in the event of multiple and small-magnitude faults. The proposal includes a new algorithm based on fuzzy rules that incorporates the daily increase of dissolved gases in the transformer oil that improves the classification of incipient faults. With reliable samples of gas dissolved in oil, the method proposed in the research can obtain a total precision rate of 91.4%. In contrast, this degree of precision is lower in other conventional methods reported in the bibliography. In addition, its performance in the classification of multiple failures is 97.5%. The method uses fuzzy logic tools to suggest actions aimed at preventive maintenance by monitoring the total of combustible gases dissolved in the oil. The proposal is a simple and easy solution to implement in practice that allows determining the status of the transformer in service without affecting the continuity of the electricity supply.

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Section: Design Of the Fault Diagnosis Systemmentioning

confidence: 99%

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

Fernández¹,

Corrales-Barrios

Benítez-Pina

et al. 2022

IREE

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“…Occurrence of different internal faults in TPS generate various gases and the amount of those gases including hydrogen (H 2 ), ethene or ethylene (C 2 H 4 ), ethane (C 2 H 6 ), ethyne or acetylene (C 2 H 2 ), march gas or methane (CH 4 ) can be utilized for fault detection and classification. Furthermore, implementation of gas rates could be lead to more accurate classification accuracy (Jerry and Gope 2018;Mahmoudi et al 2019;Wani et al 2019). The utilized 14 items of DGAT are listed in Table 1.…”

Section: Databasementioning

confidence: 99%

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Katooli

Koochaki

2020

J Control Autom Electr Syst

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Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the subsystems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure.

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“…The Artificial Neural Network (ANN) model used in a multivariable system can be generalized for a larger range of dependent parameters provided that the ANN network is well trained and optimized. The intelligent techniques reported in [21][22][23][24][25] give some useful contribution towards the assessment of residual life of cellulose paper. However, most of these works rely on the DP for assessing the insulation state.…”

Section: Introductionmentioning

confidence: 99%

Classification of Cellulosic Insulation State Based on Smart Life Prediction Approach (SLPA)

et al. 2021

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The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO2), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO2, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10−7. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.

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Multiple incipient fault classification approach for enhancing the accuracy of dissolved gas analysis (DGA)

Cited by 41 publications

References 23 publications

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Classification of Cellulosic Insulation State Based on Smart Life Prediction Approach (SLPA)

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