Fault Detection and Classification scheme using KNN for AC/HVDC Transmission Lines

Naik, Supriya; Koley, Ebha

doi:10.1109/icces45898.2019.9002230

Cited by 19 publications

(6 citation statements)

References 14 publications

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“…In addition to NN, a few other ML techniques such as fuzzy logic [145], support vector machine [146][147][148], K nearest neighbor [149,150], etc. are also used for HVDC protection.…”

Section: Machine Learning Based Approachmentioning

confidence: 99%

A Comprehensive Survey of HVDC Protection System: Fault Analysis, Methodology, Issues, Challenges, and Future Perspective

et al. 2023

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The extensive application of power transfer through high-voltage direct current (HVDC) transmission links in smart grid scenarios is due to many factors such as high-power transfer efficiency, decoupled interconnection, control of AC networks, reliable and flexible operation, integration of large wind and photovoltaic (PV)-based off-shore and on-shore farms, cost-effectiveness, etc. However, it is vital to focus on many other aspects like control, protection, coordinated operation, and power management to acquire the above benefits and make them feasible in real-time applications. HVDC protection is needed to focus further on innovative and devoted research because the HVDC system is more vulnerable to system faults and changes in operational conditions in comparison to AC transmission because of the adverse effects of low DC-side impedances and sensitive semi-conductor-based integrated power electronics devices. This paper provides a comprehensive review of the techniques proposed in the last three decades for HVDC protection, analyzing the advantages and disadvantages of each method. The review also examines critical findings and assesses future research prospects for the development of HVDC protection, particularly from the perspective of smart-grid-based power systems. The focus of the review is on bridging the gap between existing protection schemes and topology and addressing the associated challenges and issues. The aim is to inform power engineers and researchers about potential research avenues to tackle the challenges in HVDC protection in smart-grid-based power systems.

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“…In addition to NN, a few other ML techniques such as fuzzy logic [145], support vector machine [146][147][148], K nearest neighbor [149,150], etc. are also used for HVDC protection.…”

Section: Machine Learning Based Approachmentioning

confidence: 99%

A Comprehensive Survey of HVDC Protection System: Fault Analysis, Methodology, Issues, Challenges, and Future Perspective

et al. 2023

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“…Another study analyzed the modeling of coupling between AC/DC to study the mutual influence in parallel transmission lines [144]. Therefore, the protection system of hybrid transmission lines is gaining interest among researchers owing to the inapplicability of existing AC protection schemes to hybrid systems [145,146]. Any temporary fault on the AC or DC line leads to separation from the parallel network.…”

Section: Overview Of Hybrid (Ac/dc) Auto-reclosing Schemesmentioning

confidence: 99%

A Comprehensive Review of Auto-Reclosing Schemes in AC, DC, and Hybrid (AC/DC) Transmission Lines

et al. 2021

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The integration of distributed generators has changed the paradigm of modern power transmission systems. To cope with energy demands, electrical networks emphasize the efficient utilization of power transmission. Thus, high-voltage DC (HVDC) and hybrid (AC/DC) transmission systems are also getting attention owing to their high efficiency in addition to the widely adopted high-voltage AC (HVAC) systems. Most faults in the bulk of transmission lines are temporary or intermittent. Auto-reclosing schemes can be used to prevent these faults. However, conventional auto-reclosing schemes based on constant dead time cannot recognize the fault nature within the assigned duration. Consequently, the accuracy of power grids can be compromised. Therefore, adaptive auto-reclosing schemes are convenient for overcoming the issues caused by the rapid restoration of faulty power lines. This can enhance system reliability and avoid power failures and blackouts. This study is based on a systematic, detailed, and thorough research review of the existing auto-reclosing schemes in all three power transmission lines, i.e., AC, DC, and hybrid (AC/DC). Subsequently, a critical analysis has been performed to assess the pros and cons of each existing adaptive auto-reclosing scheme. Finally, future recommendations are presented to improve adaptive auto-reclosing schemes in each medium.

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“…To verify the effectiveness of the deep identification method, the proposed method is compared with the traditional machine learning methods such as support vector machine (SVM) [18], k-nearest neighbor (KNN) [31] and decision tree (DT) [32]. All methods share the same training process and testing samples, and Table 8 shows the identification accuracy.…”

Section: Figure 6 Confusion Matrix Of Deep Identificationmentioning

confidence: 99%

Research on Double-Deck Traceability Identification Method of Commutation Failure in HVDC System

et al. 2021

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In AC/DC hybrid power system, AC system failures and commutation valve trigger pulse disorder will lead to commutation failure, which may lead to DC voltage fluctuations, power transmission interruption and other serious consequences. In order to accurately and effectively identify the specific causes of commutation failures, a double deck traceability identification method is proposed in this paper. The surface identification based on wavelet entropy and affinity propagation (AP) algorithm can distinguish internal and external faults. The deep identification uses convolution neural network which can further lock the specific cause of commutation failures. In this paper, 1) the various factors leading to commutation failures are analyzed; 2) the fault feature space consists of the wavelet analysis components of DC voltage signal, and the AP algorithm is used to identify the surface source; 3) the DC current, AC voltage and current signals are added into the sample matrix of fault time-space, and the convolution neural network is used to identify the deep traceability. Finally, the accuracy of the method is verified by using the typical HVDC model.

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Fault Detection and Classification scheme using KNN for AC/HVDC Transmission Lines

Cited by 19 publications

References 14 publications

A Comprehensive Survey of HVDC Protection System: Fault Analysis, Methodology, Issues, Challenges, and Future Perspective

A Comprehensive Survey of HVDC Protection System: Fault Analysis, Methodology, Issues, Challenges, and Future Perspective

A Comprehensive Review of Auto-Reclosing Schemes in AC, DC, and Hybrid (AC/DC) Transmission Lines

Research on Double-Deck Traceability Identification Method of Commutation Failure in HVDC System

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