An Intelligent Genetic Fuzzy Classifier for Transformer Faults

Kukker, Amit; Sharma, Rajneesh; Malik, Hasmat

doi:10.1080/03772063.2020.1732844

Cited by 18 publications

(5 citation statements)

References 31 publications

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“…Mathematical implementation for a fuzzy-logic based system is available in several publications. In this regard, the reader may follow the references [33][34][35][36][37][38][39][40]. the output variable (duty-cycle) is a triangular MF followed by a normalized fuzzy set of Small (S), Medium (M), and High (H).…”

Section: B Mppt Controller Based On Flcmentioning

confidence: 99%

“…Mathematical implementation for a fuzzy-logic based system is available in several publications. In this regard, the reader may follow the references [33][34][35][36][37][38][39][40]. The produced fuzzy sets must be compared to the rule-base after the crisp inputs have been fuzzified.…”

Section: B Mppt Controller Based On Flcmentioning

confidence: 99%

“…Mathematical implementation for a fuzzy-logic based system is available in several publications. In this regard, the reader may follow the references [33][34][35][36][37][38][39][40].…”

Section: B Mppt Controller Based On Flcmentioning

confidence: 99%

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Fuzzy-Logic-Based Comparative Analysis of Different Maximum Power Point Tracking Controllers for Hybrid Renewal Energy Systems

Khan¹,

Mathew

Alotaibi

et al. 2022

Mathematics

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There is an increasing demand for power production day by day all over the globe; thus, hybrid frameworks have an essential role in producing sufficient power for the desirable load due to increasing power demand. The proposed hybrid renewable energy (HRE) systems are used to provide power in different areas to conquer the intermittence of wind and solar resources. The HRE system incorporates more than one renewable energy (RE) system. In this research article, the optimum power generation of different combinations of RE using different Maximum Power Point Tracking (MPPT) control methods is presented. The Fuel Cell (FC), FC–Photovoltaic (PV), FC–Wind (W), and FC–PV–W systems are developed to examine different MPPT controllers. The results show that the FC–PV–W HRE system produces the maximum power as compared to the FC, FC–PV, and FC–W systems. The FC–PV–W HRE system produces increased power compared to 94.24% from the FC system, 37.17% from the FC–PV hybrid system, and 15.8% from the FC–W hybrid framework with a Perturb and Observe (P&O) controller and, similarly, 74.57% from the FC system, 10.3% from the FC-PV hybrid system, and 31.64% from the FC-W hybrid system using a fuzzy logic (FL) controller, indicating that the best combination is the FC-PV-W hybrid system using an FL controller, which is useful for maximum power generation with reduced oscillations.

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Section: B Mppt Controller Based On Flcmentioning

confidence: 99%

“…Mathematical implementation for a fuzzy-logic based system is available in several publications. In this regard, the reader may follow the references [33][34][35][36][37][38][39][40]. The produced fuzzy sets must be compared to the rule-base after the crisp inputs have been fuzzified.…”

Section: B Mppt Controller Based On Flcmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy-Logic-Based Comparative Analysis of Different Maximum Power Point Tracking Controllers for Hybrid Renewal Energy Systems

Khan¹,

Mathew

Alotaibi

et al. 2022

Mathematics

Self Cite

View full text Add to dashboard Cite

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“…Parveen et.al (6) proposed a CAD model for the detection and classification of pneumonia and author concluded that SVM classifier gives the best result with HOG feature extraction as 95.35% accuracy when compared to decision tree and random forest classifier. Kukker et.al (7]) proposed a modified fuzzy Q learning (MFQL) algorithm with high accuracy when compared to SVM and KNN classifiers. MFQL gives classification accuracy for no pneumonia as 98.4%, mild pneumonia as 95.7%, and severe pneumonia as 96.17%; 91.6% for TB present and 90.1% for TB absent.…”

Section: Related Workmentioning

confidence: 99%

Automated Pneumonia Detection Method Using Hybrid Semantic Segmentation Network

M¹,

Tamilsevi²,

M³

et al. 2022

ECS Trans.

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Pneumonia is a potentially life-threatening condition that causes the air sacs in the lungs to become full of pus or fluid. Pneumonia is one of the lung infection diseases that can sometimes lead to severe or life-threatening illness and even death. Chest X-rays are mainly used for the diagnosis of pneumonia. Early detection of pneumonia is a challenge in the X-ray imaging due to the limited color scheme of X-ray imaging. Another major drawback in the early diagnosis of pneumonia is the human-dependent detection. Thus, it is the need of the hour to diagnose pneumonia at an early stage. Inspired by this issue, in this work, a novel hybrid semantic segmentation network is proposed for early detection and classification of pneumonia. Various performance metrics have been used to analyze the performance of the proposed network. Experimental results prove the efficiency of the hybrid semantic segmentation network compared with the other existing approaches in the recent works.

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“…In recent years, artificial intelligence-based techniques have been extensively studied by many researchers for transformer fault diagnosis [15][16][17][18][19][20][21][22]. These techniques include expert systems, fuzzy logic, artificial neural network, or hybrid system.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases

Gouda¹,

El-Hoshy

Ghoneim

2021

IEEE Access

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Investigating and enhancing the accuracy of widespread dissolved gas analysis (DGA) techniques based on IEC-TC10 and related databases was implemented. The current work aimed to help the experts to diagnose the transformers' faults accurately. The major drawback of the dissolved gas techniques is no decision diagnosis for the cases that lie out of the specified codes of the traditional DGA methods. In this article, fuzzy logic (FL) and artificial neural network (ANN) were applied to the standard DGA techniques such as the Dornenburg ratio method, Rogers ratio method, and IEC Standard Code 60599. This paper provided a new concept using artificial intelligence for enhancing the diagnostic accuracy of the conventional DGA method such as Dornenburg ratio, Rogers' ratio, and IEC standard which suffer from a poor diagnostic accuracy and fail to interpret the cause of the faults in most cases. In this article the FL and ANN accuracy results were compared with that of other diagnostic techniques in the literature. The results revealed that the artificial intelligence methods improve the diagnostic accuracy of the conventional DGA techniques from 41.95, 76.76, and 51.44% to 58.97 (ANN), 89.02 (FL), and 62.67% (FL) for Rogers, Dornenburg, and IEC standard code, respectively.

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An Intelligent Genetic Fuzzy Classifier for Transformer Faults

Cited by 18 publications

References 31 publications

Fuzzy-Logic-Based Comparative Analysis of Different Maximum Power Point Tracking Controllers for Hybrid Renewal Energy Systems

Fuzzy-Logic-Based Comparative Analysis of Different Maximum Power Point Tracking Controllers for Hybrid Renewal Energy Systems

Automated Pneumonia Detection Method Using Hybrid Semantic Segmentation Network

Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases

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