Stator Single-Line-to-Ground Fault Protection for Bus-Connected Powerformers Based on S-Transform and Bagging Ensemble Learning

Wang, Yuan‐Yuan; Guo, Yongsheng; Zhang, Xiangjun; Chen, Jun; Kong, Yang; Sun, Shanfeng

doi:10.1109/access.2020.2993692

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Initially, examples are created for each new bootstrap that acts as other approximately independent datasets taken from the true distribution. Thus, each weak learners is adjusted for the samples and these are aggregated obtaining an average of their results [54]. In Figure 4 the bagging ensemble learning model is presented, it is possible to see that the strategy consists of adjusting several base models in different bootstrap samples to build a model that is the average of these results.…”

Section: Ensemble Learning Modelmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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Outdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).

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Section: Ensemble Learning Modelmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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“…In Pourdarbania et al (2020), it was argued that the issue of overfitting could be overtaken with MV. Wang et al (2020) have shown that the bagging method, which pioneers to ELM, can enhance fault detection accuracy. It was demonstrated that the bagging method is better than classical machine learning methods.…”

Section: The Proposed Fault Detection and Classification Schemementioning

confidence: 99%

“…The same study indicated that their proposed classification method is superior to individual classification methods, including bagging, boosting and majority voting (MV). InPourdarbania et al (2020), it was argued that the issue of overfitting could be overtaken with MV Wang et al (2020). have shown that the bagging method, which pioneers to ELM, can enhance fault detection accuracy.…”

mentioning

confidence: 99%

Probabilistic weighted voting model using multiple machine learning methods for fault detection and classification

Ülker

Küçüker

2022

COMPEL

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Purpose The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme. Design/methodology/approach This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score. Findings The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model. Originality/value The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

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“…According to Formula (4), the current I i is irrelevant to fault transition resistance R f [25][26][27]. The injected current can fully compensate the active and reactive components of the fault current.…”

Section: Compensation Current Calculation Modulementioning

confidence: 99%

Simulation and Experiment Analysis of 10 kV Flexible Grounding Device

2021

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The traditional 10 kV distribution network grounding system has some disadvantages, such as small grounding current and poor arc extinguishing effect, thus, hindering the detection of high-resistance grounding fault. Therefore, this paper studied the flexible grounding system consisting of small-resistance and active inverter in parallel. The control system comprises the compensation current calculation module, the fault detection module, and line protection strategy. During a single-phase grounding fault, the device is designed to inject a current of a given amplitude and phase into the neutral point to effectively suppress fault-point voltage and current and, meanwhile, quickly identifying the fault line or the busbar fault and then systematically protecting the distribution line. In addition, a large number of simulations have performed based on three grounding faults (metal, low-resistance, and high-resistance) and two modes (ungrounded and small-resistance grounding). The device can all be functional. Finally, a 400 V-level experimental prototype was built, and the experimental results are consistent with the simulation results, which can verify the effectiveness and feasibility of the flexible grounding device.

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Stator Single-Line-to-Ground Fault Protection for Bus-Connected Powerformers Based on S-Transform and Bagging Ensemble Learning

Cited by 10 publications

References 25 publications

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Probabilistic weighted voting model using multiple machine learning methods for fault detection and classification

Simulation and Experiment Analysis of 10 kV Flexible Grounding Device

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