An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis

Tightiz, Lilia; Nasab, Morteza Azimi; Yang, Hyo-Sik; Addeh, Jalil

doi:10.1016/j.isatra.2020.03.022

Cited by 90 publications

(34 citation statements)

References 46 publications

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“…Then, the proposed approach with varying values of λ ∈ [1,10] is executed when k = 50 and γ = 10. From Figure 7b, robust diagnosis accuracies can be gained with λ ∈ [3,6]. Finally, varying values of regularization parameter γ ∈ [1,10] are implemented for MRMI with the other parameter settings of k = 50 and λ = 1.…”

Section: ) Confusion Matrixmentioning

confidence: 99%

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A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Zhang

Shao

Wang

et al. 2021

Sensors

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As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

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Section: ) Confusion Matrixmentioning

confidence: 99%

“…In addition, bearing and gear faults are the most common failure mode which may lead to unexpected fatal failures and elevated maintenance costs. Thus, there is a strong demand for intelligent fault diagnosis techniques of bearings and gears to ensure the security and reliability of mechanical equipment [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Zhang

Shao

Wang

et al. 2021

Sensors

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“…Conversely, analytical methods based on absolute gas concentrations are better at discriminating between normal and failure condition. The amount of data collected by utilities motivated researchers to develop analytical methods using mathematical models and supervised learning techniques, namely: hybrid models combining different engineering methods,10 evolutionary methods,12 Markov models,13, 14 fuzzy models,15, 16 multilayered artificial neural networks,17, 18 support vector machine (SVM),19 twin support vector machine (TWSVM),20 wavelet networks,21 Bayesian networks,22 probabilistic classifiers,23, 24 nearest neighbor clustering,25 and association rules 26, 27. Some of the mathematical models and supervised learning techniques mentioned before rely on a small sample of failure data to be trained and tested, which can jeopardize their robustness and generalization capability.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised approach for fault diagnosis of power transformers

Dias

Ribeiro

Leitão

et al. 2021

Quality & Reliability Eng

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Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

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“…This study presents an intelligent method for detecting and classifying power transformer faults based on the Informative Analysis Gas Analysis Method [9] Integrating solar charging stations with solid‐state transformer (SST) is appropriate because they have multiple AC and DC and power conversion. Also, the flexible SST controller enhances solar charging stations in the smart grid because the EV battery and photovoltaic array energy can be synchronised.…”

Section: Introductionmentioning

confidence: 99%

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

Zand

Nasab

Padmanaban

et al. 2020

IET Renewable Power Generation

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This study introduces a type of solid‐state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic‐assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real‐time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real‐time power allocation which improves the efficiency of the photovoltaic‐based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

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An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis

Cited by 90 publications

References 46 publications

A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

An unsupervised approach for fault diagnosis of power transformers

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

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