An artificial neural network model with Yager composition theory for transformer state assessment

Lin, Zhiming; Tang, Songping; Peng, Gang; Zhang, Yun; Zhong, Ziyang

doi:10.1109/iaeac.2017.8054097

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Although the Yager algorithm can decrease the credibility of evidence with significant conflicts, it brings in more uncertainty. Therefore, some researchers [18] improved the Yager fusion algorithm. Mark evidence as m1, m2, …, mn, and recognition result sets as F1, F2, …, Fn.…”

Section: Improvement On Fusion Rulesmentioning

confidence: 99%

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

et al. 2021

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Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

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Section: Improvement On Fusion Rulesmentioning

confidence: 99%

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

et al. 2021

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“…Further, the matrix can be written as the product of the matrices m T k, q and mk, qÀ1 . For the row r in equation (21), we only use the rth element in m T k, q to multiply the rth element and latter two elements in mk, qÀ1 , and then get their sum. On the other hand, we compute the product of m T k, q and mk, qÀ1 .Then, the associated results are added to obtain mk, q .…”

Section: Combination Of Bpamentioning

confidence: 99%

“…Concerning on the certain risk degree in the inland waterway, a navigation assessment model was researched. 21 In the D-S evidence theory, each kind of available evidence has its contribution and affects the assessment result. Thus, Yager assigned the conflicting evidence to the identification framework and classified unusable evidences into the unknown territory.…”

Section: Introductionmentioning

confidence: 99%

Novel assessment and prediction method for vessel traffic risk degree

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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To assess the current risk degree and predict the future risk degree of vessel traffic, a novel method is put forward in this study. Different from the existing literature, the available evidence of vessel traffic is directly transformed into the weighted basic probabilistic assignment (BPA) based on the optimal solution to the intersection of fuzzy membership functions in the framework of D-S evidence theory. The matrix deformation algorithm towards the combination rule makes the time complexity low in the process of the risk degree assessment. With respect to the risk degree prediction, the required Sigma points are effectively extracted. We derive the adaptive filtering gain that is suitable for the rapidly changing BPA. Finally, the experiments of vessel traffic in the Dalin Bay are made to indicate performance of the proposed method.

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“…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. (Lin et al, 2017) pointed out that the traditional transformer condition assessment standards were single, and different types of condition information in power transformers gave different assessment results, which made it difficult for equipment managers to make decisions. To address these shortcomings, an artificial neural network model for transformer condition assessment based on Yager synthesis theory was proposed.…”

Section: Introductionmentioning

confidence: 99%

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

et al. 2022

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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.

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An artificial neural network model with Yager composition theory for transformer state assessment

Cited by 8 publications

References 6 publications

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

Novel assessment and prediction method for vessel traffic risk degree

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

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