Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis

Souahlia, Seifeddine; Khmais, Bacha; Châari, Abdelkader

doi:10.5120/4585-6768

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…For the middle-term stage, assuming we have a variety of different forecasting algorithms, and based on the same known data, the forecasting results generated by different forecasting algorithms are [12] :…”

Section: A Power Demand Forecasting Modelmentioning

confidence: 99%

Design and Implementation of Smart Electricity with Monitor Integrating Forecasting and Status Estimation Functions and the Relevant Simulation Based on MATLAB

Zhang

Dang

2014

AMM

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With the development and proliferation of smart grid and the relevant techniques, smart the electricity monitor is increasingly important for improving the overall performance of power network. With a number of new attributes, the smart electricity monitor is significantly different from these conventional electricity meters. For example, it actively provides the information for the management, reliability and maintainability purposes and acts as an irreplaceable component in the power network. In this paper, we mainly focus on the design and implementation of the smart grid which integrates a series of innovative functions, for example demand forecasting and status estimation. Specifically, the research backgrounds pertaining to this field will be briefly introduced and the state-or-the-art mathematical models related to both demand forecasting and status estimation will be constructed accordingly. Also, to investigate their feasibility and efficiency, a set of simulations based on MATLAB will be conducted and the simulated results will be presented. Finally, a complete and innovative prototype of smart electricity use monitor integrating forecasting and status estimation functions can be constructed, verified and analysed in depth by both of mathematical expressions and simulated results.

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Section: A Power Demand Forecasting Modelmentioning

confidence: 99%

Design and Implementation of Smart Electricity with Monitor Integrating Forecasting and Status Estimation Functions and the Relevant Simulation Based on MATLAB

Zhang

Dang

2014

AMM

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“…The basic fuzzy system also has some shortcomings, such as the functions of members must be determined based on expert advice or practical experience [16]. The SVM has the advantages of solid generalization ability and fast training speed, and it has unique advantages in the case of insufficient transformer fault samples [17]. Unfortunately, the super‐parameter of SVM has a significant impact on the diagnostic performance, and there is still no recognized method to determine the super‐parameter.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Oil‐Immersed Power Transformers Using SVM and Logarithmic Arctangent Transform

Ruan

et al. 2022

IEEJ Transactions Elec Engng

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A new method of dissolved gas analysis is proposed to improve the accuracy of transformer fault diagnosis. The slime mold optimized support vector machine (SMA-SVM), and logarithmic arctangent transform (LOG-ACT) are combined. On the one hand, the better global optimization performance of SMA is used to optimize SVM parameters to solve the difficulty of SVM parameter selection. On the other hand, corresponding transformations are carried out for different features: the logarithmic(LOG) transformation is carried out for the original DGA data to retain the order of magnitude information. The arctangent (ACT) transformation is carried out for the ratio features to improve the data structure. Therefore, the combination of data transformation and optimization model can improve the accuracy of diagnosis from two aspects of data structure and classification algorithm. The performance of the proposed method was compared with IEC three ratio method, artificial neural network, optimized artificial neural network, GA-SVM, and PSO-SVM. Experimental results using published data show that the proposed method can significantly improve the accuracy of transformer fault diagnosis.

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“…However, it is inconvenient and time-consuming for industrial applications due to the complex analytical process. Hence, artificial intelligence techniques have been proposed to develop more accurate diagnostic tools based on DGA data [22]. In [23][24][25][26][27][28][29][30][31][32], some artificial intelligence techniques such as fuzzy logic, artificial neural network and support vector machines have been introduced for fault classification with nearly equal performance without determination of problem severity.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Approach to Dissolved Gas Analysis for Power Transformer Failure Index and Fault Identification

Poonnoy

Suwanasri

2020

Energies

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This research focuses on problem identification due to faults in power transformers during operation by using dissolved gas analysis such as key gas, IEC ratio, Duval triangle techniques, and fuzzy logic approaches. Then, the condition of the power transformer is evaluated in terms of the percentage of failure index and internal fault determination. Fuzzy logic with the key gas approach was used to calculate the failure index and identify problems inside the power transformer. At the same time, the IEC three-gas ratio and Duval triangle are subsequently applied to confirm the problems in different failure types covering all possibilities inside the power transformer. After that, the fuzzy logic system was applied and validated with DGA results of 244 transformers as reference cases with satisfactory accuracy. Two transformers were evaluated and practically confirmed by the investigation results of an un-tanked power transformer. Finally, the DGA results of a total of 224 transformers were further evaluated by the fuzzy logic system. This fuzzy logic is a smart, accurate tool for automatically identifying faults occurring within transformers. Finally, the recommendation of maintenance strategy and time interval is proposed for effective planning to minimize the catastrophic damage, which could occur with the power transformer and its network.

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Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis

Cited by 9 publications

References 13 publications

Design and Implementation of Smart Electricity with Monitor Integrating Forecasting and Status Estimation Functions and the Relevant Simulation Based on MATLAB

Design and Implementation of Smart Electricity with Monitor Integrating Forecasting and Status Estimation Functions and the Relevant Simulation Based on MATLAB

Fault Diagnosis of Oil‐Immersed Power Transformers Using SVM and Logarithmic Arctangent Transform

Fuzzy Logic Approach to Dissolved Gas Analysis for Power Transformer Failure Index and Fault Identification

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