Insulation Condition Assessment of Power Transformers Employing Fused Information in Time and Space Dimensions

Abstract Based on the association rules and variable weight synthesizing theory, a new insulation condition assessment (ICA) method for power transformers is presented, employing fused information in both time and space dimensions. With the uncertainty and fuzziness of available data from the power transformer concerned taken into account, a multilateral transformer ICA model based on fuzzy and evidence reasoning is developed. The ICA model contains three layers: target layer, factor layer, and index layer. The association rules and variable weight synthesizing theory are applied to determine the variable weight coefficients of factors and its indicators in the assessment model. A fuzzy membership function is formulated to describe the factor layer on assessment model. The evidence reasoning and the multilateral assessment scheme are adopted to merge the information of each factor in time and space dimensions, and then the assessing result of the transformer is obtained. Numerical results based on practical scenarios demonstrate that the proposed method is feasible and efficient.

[1]  Ruijin Liao,et al.  Extraction of Frequency Domain Dielectric Characteristic Parameter of Oil-paper Insulation for Transformer Condition Assessment , 2015 .

[2]  Hanbo Zheng,et al.  A cloud and evidential reasoning integrated model for insulation condition assessment of high voltage transformers , 2014 .

[3]  Tong Liu,et al.  A Dynamic Integrated Fault Diagnosis Method for Power Transformers , 2015, TheScientificWorldJournal.

[4]  Lee Li,et al.  An integrated method of set pair analysis and association rule for fault diagnosis of power transformers , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[5]  Maria Pinto,et al.  A grounded theory on abstracts quality: Weighting variables and attributes , 2006, Scientometrics.

[6]  Mominul Islam,et al.  Application of a general regression neural network for health index calculation of power transformers , 2017 .

[7]  W.H. Tang,et al.  A Probabilistic Classifier for Transformer Dissolved Gas Analysis With a Particle Swarm Optimizer , 2008, IEEE Transactions on Power Delivery.

[8]  Ting Wang,et al.  Conjunctival Flap Covering Combined with Antiviral and Steroid Therapy for Severe Herpes Simplex Virus Necrotizing Stromal Keratitis , 2015, TheScientificWorldJournal.

[9]  W.H. Tang,et al.  An evidential reasoning approach to transformer condition assessments , 2004, IEEE Transactions on Power Delivery.

[10]  Jawad Faiz,et al.  Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[11]  Osama E. Gouda,et al.  Proposed heptagon graph for DGA interpretation of oil transformers , 2017 .

[12]  Enze Zhang,et al.  A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method , 2019, Energies.

[13]  Khmais Bacha,et al.  Power transformer fault diagnosis based on dissolved gas analysis by support vector machine , 2012 .

[14]  Rajeevan Chandel,et al.  Condition assessment of power transformers based on multi-attributes using fuzzy logic , 2017 .

[15]  Xiang Zhou,et al.  A Trust Evaluation Algorithm for Wireless Sensor Networks Based on Node Behaviors and D-S Evidence Theory , 2011, Sensors.

[16]  A. O. Akumu,et al.  Application of fuzzy logic and evidential reasoning methodologies in transformer insulation stress assessment , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[17]  E Gockenbach,et al.  Intelligent agent-based system using dissolved gas analysis to detect incipient faults in power transformers , 2010, IEEE Electrical Insulation Magazine.

[18]  Ruijin Liao,et al.  An Integrated Decision-Making Model for Condition Assessment of Power Transformers Using Fuzzy Approach and Evidential Reasoning , 2011, IEEE Transactions on Power Delivery.

[19]  Behrooz Vahidi,et al.  Techno-economical lifetime assessment of power transformers rated over 50 MVA using artificial intelligence models , 2016 .

[20]  Yuanzhang Sun,et al.  Study on the plan of UHV substation location based on grey comprehensive optimal selection using moment estimation theory to weight , 2012, 2012 IEEE International Conference on Power System Technology (POWERCON).

[21]  Zhao Ma,et al.  Research on multi-attribute decision-making in condition evaluation for power transformer using fuzzy AHP and modified weighted averaging combination , 2016 .

[22]  Hui Ma,et al.  Investigation of feature selection techniques for improving efficiency of power transformer condition assessment , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[23]  Syed Islam,et al.  Fuzzy logic approach in power transformers management and decision making , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.