A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Huang, Xiaoge; Zhang, Yiyi; Liu, Jiefeng; Zheng, Hanbo; Wang, Ke

doi:10.3390/polym10101096

Cited by 14 publications

(11 citation statements)

References 41 publications

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“…The optimal hybrid DGA feature subset (OHFS) was selected from three feature sets by using genetic algorithm-support vector machine-feature screen (GA-SVM-FS) model and used as input of the improved social group optimization (ISGO) optimized multi-SVM classifier to develop a transformer fault diagnosis model which achieved the highest fault diagnosis accuracy (92.86%) compared with other diagnostic models [24]. In addition, other scholars also used the SVM [49], relevance vector machine (RVM) [50] for transformer fault diagnosis and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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“…A support vector machine (SVM), a form of supervised machine learning, has been widely applied for predicting creep behavior [28], moisture [29], etc. For instance, a 3-stage GA-SA-SVM has been used in dissolved gas analysis, with 90.36% accuracy [30]. Despite SVM involvement in numerous applications, the use of SVMs on a self-powered IPMC sensor is yet to be explored for healthcare applications.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Assisted Throat Sensor Using Ionic Polymer–Metal Composite (IPMC) Material

et al. 2021

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Throat sensing has received increasing demands in recent years, especially for oropharyngeal treatment applications. The conventional videofluoroscopy (VFS) approach is limited by either exposing the patient to radiation or incurring expensive costs on sophisticated equipment as well as well-trained speech-language pathologists. Here, we propose a smart and non-invasive throat sensor that can be fabricated using an ionic polymer–metal composite (IPMC) material. Through the cation’s movement inside the IPMC material, the sensor can detect muscle movement at the throat using a self-generated signal. We have further improved the output responses of the sensor by coating it with a corrosive-resistant gold material. A support vector machine algorithm is used to train the sensor in recognizing the pattern of the throat movements, with a high accuracy of 95%. Our proposed throat sensor has revealed its potential to be used as a promising solution for smart healthcare devices, which can benefit many practical applications such as human–machine interactions, sports training, and rehabilitation.

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“…Compared with GA and PSO algorithms, the ABC algorithm was fit to optimize SVM (ABC-SVM) to improve the classification accuracy and complexity simultaneously. Huang et al [ 45 ] optimized the SVM parameters through ABC and realized the analysis of dissolved gases. Li et al [ 46 ] compared the four algorithms of ABC, GA, PSO, and ACO and found that the ABC algorithm has a simpler operation, fewer parameters, and stronger search capability than other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

Gao

Tian

Yao

et al. 2021

Computational Intelligence and Neuroscience

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Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

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A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Cited by 14 publications

References 41 publications

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Artificial Intelligence-Assisted Throat Sensor Using Ionic Polymer–Metal Composite (IPMC) Material

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

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