Transformer condition assessment using fuzzy C-means clustering techniques

Eke, Samuel; Clerc, Guy; Aka-Ngnui, T.; Fofana, I.

doi:10.1109/mei.2019.8636165

Cited by 18 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The condition assessment methods of power transformer based on DGA, electrical testing, oil testing, and miscellaneous factors have been introduced in the previous literature [1,2,6,7,17,18,20]. It is known from these documents that fuzzy logic based methods are superior to conventional ratio methods [6,7].…”

Section: Condition Assessment For Power Transformermentioning

confidence: 99%

“…In addition, there are differences in the indexes and parameters that are calibrated by the expert's experience [18]. To solve these uncertainties, some fuzzy logic, Bayesian network, intelligent algorithm, and other methods based on electrical, chemical, physical, and other test parameters are developed and applied to the condition assessment of power transformers [15,[17][18][19][20]. In order to reduce the assessment complexities of power transformers, some methods are developed to extract the most influential assessment factors based on feature selection and classification techniques [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multifactorial condition assessment for power transformers

Zhou

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Section: Condition Assessment For Power Transformermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multifactorial condition assessment for power transformers

Zhou

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…In recent years, researchers have used intelligent techniques such as fuzzy theory (Rexhepi and Nakov, 2018), clustering (Eke et al, 2019), and neural networks (Islam et al, 2017) to fully combine and utilize the condition quantities of transformers, which can eventually assess the current health of transformers more accurately and thus avoid large-scale power accidents due to sudden failures. Many researchers have combined these intelligent techniques and their improved methods with the dissolved gas approach (DGA) for assessing the health of oil-immersed transformers.…”

Section: Introductionmentioning

confidence: 99%

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

et al. 2022

View full text Add to dashboard Cite

Transformer is one of the important equipment in the power grid, which helps to integrate renewable energy into the transmission and distribution network efficiently. The safe and stable operation of transformer is of great importance for the reliable transmission of electricity generated from renewable energy and for the reliable use of electricity by the end users. Therefore, it is important to assess the condition to avoid the faults of the transformer. In this paper, a variable weight synthesizing assessment model is presented that combines the G1 method, the entropy weight method, and a variable-weight method proposed in this paper to assess the condition of transformer based on the offset of the transformer equivalent circuit parameters. First, we propose deterioration indexes oriented to the maintenance management needs, which can well reflect the degree of deterioration of each transformer component. Second, the various defects of the transformer are used as the assessment indexes, and the initial weight is given to the assessment indexes according to the damage degree of the defect. The initial weight is calculated comprehensively by the G1 method and the entropy weight method. Then, each index is scored according to the offset of the equivalent circuit parameters, and the weights are adjusted appropriately according to the scores of the indicators using a variable weighting method to emphasize the severity of the defect or the “sub-health” condition of the transformer. Finally, the respective scores and combined weights of the assessment indexes are weighted to obtain a comprehensive score. The simulation shows that the model is more sensitive to abnormal and “subhealth” conditions of the transformer, which verifies the feasibility of the variable weight synthesizing model to assess the condtion of the transformer.

show abstract

“…Ruspini [4] proposed the concept of fuzzy division and introduced fuzzy set theory into cluster analysis. Subsequently, scholars have successively proposed fuzzy clustering analysis methods, such as a transitive closure algorithm based on fuzzy equivalence relation [2,5,6], a fuzzy c-means clustering algorithm [7][8][9] and a hierarchical clustering algorithm [3,10]. The hierarchical clustering algorithm is a representative and essential clustering method.…”

Section: Introductionmentioning

confidence: 99%

Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Lin

Zhang

et al. 2021

Mathematics

View full text Add to dashboard Cite

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

show abstract

Transformer condition assessment using fuzzy C-means clustering techniques

Cited by 18 publications

References 11 publications

Multifactorial condition assessment for power transformers

Multifactorial condition assessment for power transformers

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Contact Info

Product

Resources

About