Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs

Wu, Jiyang; Li, Qiang; Chen, Qian; Zhang, Nan; Mao, Chizu; Yang, Litai; Wang, Jinyu

doi:10.3389/fenrg.2023.1144785

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…Cat-Boost does not require feature-to-number conversion or pre-processing for numerous fault categories. Cat-Boost minimized hyperparameter modification and used binary Decision Trees as basis predictors to solve complex, noisy, and hydrogenous problems [22]. It loads all sample datasets into the training architecture, mixes a GBDT with unconditional features, and transforms each sample's x-tics before sampling for calculation.…”

Section: The Use Of Cat-boost Architecture For Fault Classification A...mentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

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The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.

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Section: The Use Of Cat-boost Architecture For Fault Classification A...mentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…By examining and interpreting power grid data, artificial intelligence (AI) technology can enhance the intelligent management of power grids, thus reducing energy consumption and environmental pollution (Cai et al, 2022;Tang et al, 2022;Wu et al, 2023). The hybridcascaded high-voltage direct current (HVDC) transmission technique can effectively reduce power transmission losses, and a combination of the two can boost further the energy utilization efficiency and intelligence of the power grid (Tang et al, 2021;Dong et al, 2023;Liang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Study on the characteristic of the grounding fault on the cascaded midpoint side of the hybrid cascaded HVDC system

et al. 2023

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The hybrid cascaded high-voltage direct current (HVDC) system combines the system support capabilities of the modular multilevel converter (MMC) with the capacity of the line-mutated converter’s (LCC’s) advantage of high-power transmission. The HVDC system is among the key elements of a smart grid where artificial intelligence is applied extensively. However, the characteristics of a grounding fault on the cascaded midpoint side of a hybrid cascaded HVDC system remain unclear. This study analyzes fault characteristics and the impact of faults using analytical methods. First, the topology and basic control strategy are presented. The fault response process is then analyzed by dividing systems into the MMC and LCC parts at the inverter side. A separate theoretical analysis is also conducted. In addition, the impacts of faults on HVDC and alternating current (AC) networks are analyzed. Therefore, even after the HVDC system is disabled, the AC network can supply fault currents using an antiparallel diode. The simulation results show that the proposed analysis method is feasible, and the theoretical analysis is correct. The proposed method can provide a theoretical basis for the selection of equipment for HVDC systems and smart grid construction.

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“…However, to the best of the author's knowledge, the automatic fault diagnosis on hydro-power plants is still a challenge. Moreover, the traditional fault diagnosis algorithm with a knowledge graph cannot output the relationship path, used to reveal the indirect reasons for the occurring faults [19].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for hydro-power plants with Bi-LSTM knowledge graph aided by attention scheme

Guo,

Wang,

Pan

et al. 2023

J. vibroeng.

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In hydro-power systems, the fault of equipment is an important potential threat for the safe production of electricity. Therefore, the automation and intelligence of fault diagnosis becomes the popular issue in the research on hydro-power system. In this paper, a knowledge graph-based method is put forth to diagnose faults occurred in hydro-power systems, since the knowledge graph can store structured and unstructured data for better fault diagnosis and intelligently search the reasons of the faults. First, we model the knowledge graph for hydro-power plants, where the rational path for the fault reason is formulated. Then, the bi-directional long short-term memory (Bi-LSTM) with conditional random field (CRF) is used to extract the entities and relations to the given documents, which record the phenomenon and reasons for the occurred faults. Moreover, the attention scheme is employed in the Bi-LSTM to weigh the closer relationships to improve the diagnosis accuracy. An automatic diagnosis algorithm is developed to improve the diagnosing efficiency by constructing rational paths, with which directive and in-directive factors for occurring faults can be traced. Simulation results reveal that the intelligent search method with a knowledge graph can effectively find the reason, locate the position, and provide useful suggestions for the occurred faults.

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Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs

Cited by 8 publications

References 42 publications

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Study on the characteristic of the grounding fault on the cascaded midpoint side of the hybrid cascaded HVDC system

Fault diagnosis method for hydro-power plants with Bi-LSTM knowledge graph aided by attention scheme

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