An Adaptive Neuro Fuzzy Inference System for Fault Detection in Transformers by Analyzing Dissolved Gases

Vani, Ms. Alamuru; Murthy, Pessapaty Sree Rama Chandra

doi:10.9790/1676-09460714

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The ANFIS method has been applied to several power systems; in the design of a static synchronous series compensator-based controller for the improvement of transient stability [22], in the improvement of the power quality of a power production system for a split shaft microturbine [23], in power flow analysis and optimization [24], in the control of voltage and frequency in a variablespeed wind power generation [25], in fault detection in transformers by analyzing dissolved gases [26], in the control of multi-area load frequency [27], in the design of a power system stabilizer with unified power flow controller [28], and in the design of robust power system stabilizers (PSS) [29].…”

Section: Introductionmentioning

confidence: 99%

Critical Clearing Time Prediction for Power Transmission Using an Adaptive Neuro-Fuzzy Inference System

Jiriwibhakorn

2023

IEEE Access

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An adaptive neuro-fuzzy inference system (ANFIS) is a hybrid algorithm composed of fuzzy logic and an artificial neural network. It takes advantage of fuzzy logic and artificial neural networks to solve complex problems. For power transmission, several dynamic parameters are ignored for conventional transient stability assessment due to the complexity of the equations, and due to the long computational time required. Certainly, it is very difficult to do the real-time assessment of large power systems by considering dynamic impacts in detail. In this paper, an approach is required to increase the accuracy of results. Herein, a method, namely ANFIS, was found to overcome the limitations. All significant effects of dynamics can be taken into account; not only the machine model but also the turbine governor model, automatic voltage regulator (AVR) model, and load characteristic model are carefully considered. In addition, the model used for each generator unit is varied to achieve real conditions. The ANFIS output is the critical clearing time (CCT). CCT values are very important to be correctly predicted for the stability of power systems after clearing the faults. When the faults are cleared by opening the circuit breakers within the CCT values, the power systems are still stable. If the faults are cleared after the CCT values, the systems become unstable. The modified IEEE 9-bus and IEEE 39-bus systems are applied in the implementation of this study. The locations of faults and the levels of loads (except the power system topology changes) are varied for each simulation. The results from the ANFIS application indicate that ANFIS (considering the dynamic effects of the machine models, AVR system, turbine governors, and load characteristics) can predict CCT values with high accuracy. Moreover, when ANFIS results are compared to the artificial neural network (ANN) results, which are generally used, it can be seen that ANFIS results are better and take a lower time for training and testing processes than ANN. ANFIS can be adapted, improved, and implemented in real practice.INDEX TERMS Critical clearing time, automatic voltage regulator model, governor model, an adaptive neuro-fuzzy inference system, an artificial neural network

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Section: Introductionmentioning

confidence: 99%

Critical Clearing Time Prediction for Power Transmission Using an Adaptive Neuro-Fuzzy Inference System

Jiriwibhakorn

2023

IEEE Access

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“…A host of soft computing methodologies have been implemented using the diagnostic standards of IEEE and IEC (Interpretation of the analysis of, 1978;Rogers, 1978;Hooshmand and Banejad, 2008;Author anonymous, 2009). The possibility of transformer incipient fault diagnosis and transformer insulation health monitoring has been explored using Artificial Neural Network (ANN) and Machine Learning (ML) (Guardado et al, 2001;Equbal et al, 2018;Nezami et al, 2021a;Ghoneim et al, 2021;Kherif et al, 2021;Taha et al, 2021), Fuzzy Logic (FL) system (Dukarm, 1993;Dhote and Helonde, 2012;Huang and Sun, 2013;Noori et al, 2017) and Adaptive Neuro Fuzzy Inference System (ANFIS) (Hooshmand et al, 2012;Khan et al, 2014;Vani and Murthy, 2014;Khan et al, 2015;Nezami et al, 2021b). However, these methods have their own limitations which needs to be addressed for their effective use in an online system.…”

Section: Introductionmentioning

confidence: 99%

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Equbal

Nezami²,

Hashem³

et al. 2022

Front. Energy Res.

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E-Nose finds its use in a wide range of applications such as quality assessment in food processing to toxic gas identification in chemical industry either in the offline or online mode. Their usage can be extended to transformer condition monitoring in the online mode. Considering the importance of transformers in power system and the impact it could create if faults in them are unidentified or left unattended, their functioning should be monitored on a real time basis. This work, describes the realization of a prospective E-Nose for online transformer incipient fault identification. The resistive gas sensor array has been simulated in real time using variable resistances forming one arm of a Wheatstone bridges. Separate variable resistances have been calibrated using characteristics of different fault gas sensors. The sensor array of the E-Nose helps to identify the transformer fault gases resulting from an incipient fault condition at the nascent stage itself and prompts for the necessary corrective action well before a catastrophic situation arises. Furthermore, ANFIS model of the Duval’s Triangle (DT) method have been developed to facilitate the online classification of incipient faults. The ANFIS models of other popularly used incipient fault interpretation methods, reported in earlier works, have also been used for a comparative analysis on their diagnostic capabilities. The developed model has been tested using the fault cases of IEC-TC10 fault database and the results thus obtained have been found to be very promising.

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“…However, the existing deep learning methods still have some shortcomings in solving these problems, such as insulator identification methods [5], or power transformer fault diagnosis [6], they can achieve good results under limited data sets. But these methods can only have satisfactory performance for specific problems, which do not work for other kinds of defects.…”

Section: Introductionmentioning

confidence: 99%

Inspect Defect of Power Equipment via Deep Learning Method

Zhang¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Efficient defect detection is crucial for maintaining a stable power system. One method is to check the status of electrical equipment through images or videos taken by UAVs (unmanned aerial vehicles), overcoming various difficulties of manual inspection in complex environments such as high-altitude, snowy or rainy days, etc. Unfortunately, it is still a difficult challenge to process the data returned by UAVs automatically and effectively. This study proposes a defect detection algorithm to process UAV images of power equipment based on Faster-RCNN. Additionally, we use multiple optimization strategies to improve the performance of the benchmark algorithm, including feature pyramid network, deformable convolution, online hard example mining, and data augmentation. Our results show that the average accuracy of experiments obtains nearly 60% improvement compared to that naive deep learning target detection algorithm. Meanwhile, this method is suitable for various defects of power equipment with high accuracy, thus benefiting thousands of households.

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An Adaptive Neuro Fuzzy Inference System for Fault Detection in Transformers by Analyzing Dissolved Gases

Cited by 3 publications

References 9 publications

Critical Clearing Time Prediction for Power Transmission Using an Adaptive Neuro-Fuzzy Inference System

Critical Clearing Time Prediction for Power Transmission Using an Adaptive Neuro-Fuzzy Inference System

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Inspect Defect of Power Equipment via Deep Learning Method

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