Detecting Faults in VSC-HVDC Systems by Deep Learning and K-means

Sadeghi, Roohollah; Shahbazzadeh, Mehdi Jafari; Eslami, Mahdiyeh

doi:10.2174/2352096513999201105155206

Cited by 3 publications

(3 citation statements)

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“…PMU also employs advanced signal processing approaches, such as the wavelet transform, to extract crucial fault signal features from both sides of the line [25]. Researchers did a quick summary of the various approaches for analyzing power system issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Time-Frequency Transformation Technique with Various Mother Wavelets for DC Fault Analysis in HVDC Transmission Systems

Chandio,

Memon

2023

SSURJET

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HVDC transmission has become a cost-effective option for transferring high voltage over greater distances. Protecting an HVDC transmission line is more challenging than protecting an AC transmission line due to its low impedance and absence of zero crossing DC current. Power electronic devices have finite overload capability, and standard relays are ineffective for HVDC line protection. Heavy current is generated by DC faults in HVDC (T/L), hence it is necessary to treat DC line faults in short, medium, and long HVDC (T/L) systems with different fault resistance. It is crucial to research fault detection methods based on time-frequency analysis by choosing appropriate algorithms using various (MW) methodologies for short, medium, and long HVDC transmissions at various fault resistances in order to protect the HVDC system. In this work, we examine how the length of an HVDC transmission line and fault resistance at both sides (inverter and rectifier) affects the selection of suitable mother wavelets for DC fault, using mean parameter and time-frequency transformation. SimPower System of Matlab is used to evaluate the impact of HVDC transmission line length and fault resistance on the selection of suitable mother wavelets. Simulation results show that Coif3 is an ideal wavelet for fault detection in medium transmission lines at different fault resistances. On the other hand, Rbio3.1 is more suitable for fault detection in long transmission lines at various fault resistances. For short and medium HVDC transmission lines, different mother wavelets were found to be suitable at different fault resistances. Therefore, it is essential to carefully consider the specific characteristics of the transmission line and the fault scenario when selecting a mother wavelet for accurate fault location.

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Section: Literature Reviewmentioning

confidence: 99%

Time-Frequency Transformation Technique with Various Mother Wavelets for DC Fault Analysis in HVDC Transmission Systems

Chandio,

Memon

2023

SSURJET

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“…In previous research studies [23][24][25][26][27][28][29], the wavelet feature has been used to detect errors in HVDC. Tis study uses signal processing features and metaheuristic methods to fnd optimal signals, and then uses a support vector machine (SVM) machine learning model to achieve highperformance results in detecting faults in HVDC.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Hazim Hameed Hameed,

Kutbay,

Rahebi

et al. 2024

International Transactions on Electrical Energy Systems

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Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk’s optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system’s effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability.

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“…Compared to High Voltage Alternating Current (HVAC) systems, HVDC systems possess numerous advantages, such as improved reliability, reduced transmission losses, and lower transmission costs. With the recent development of power semiconductor devices, the overall efficiency of the HVDC system has been improved [2], and [3]. The present-day HVDC transmission technology is a reliable option for transmitting electrical power.…”

Section: Introductionmentioning

confidence: 99%

Application of Time-Frequency Analysis with Selection of Suitable Mother Wavelets for Transient States of HVDC Transmission Lines

Chachar,

Memon,

Ansari

2023

SSURJET

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Recent developments in power semiconductor devices have made High Voltage Direct Current (HVDC) transmission systems the most crucial technology for future power systems. Today several HVDC projects have been commissioned around the world. The primary concern for such an advanced HVDC grid is to follow standard grid codes to maintain the safety and stability of the electrical equipment during transient and dynamic circumstances. Although the HVDC system has been recognized as the most prominent technology to transmit electric power, some issues must be addressed, such as selective and quick detection of faults. For selective fault detection, it is essential to look into the several fault detection strategies that fortify the HVDC system with time-frequency signal processing techniques. This research uses a time frequency-based method to identify the different HVDC transmission line faults. The most suitable mother wavelet for various transient states is selected by analyzing fault voltage and current signals based on variance, standard deviation, and range parameters. An experimental setup of a thyristor-based HVDC transmission system has been developed in the MATLAB/Simulink environment, and multiple fault scenarios have been analyzed. The simulation outcomes reveal that the most effective mother wavelet for detecting DC line, symmetrical and unsymmetrical faults occurs at the 6th level of fault signal decomposition. By examining current and voltage signals, our work has determined the best mother wavelet for various faults. Using more than one mother wavelet to detect faults is more reliable than using a single mother wavelet to detect all faults. Additionally, it has been noted that each fault's voltage and current signals have two distinct mother wavelets.

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Detecting Faults in VSC-HVDC Systems by Deep Learning and K-means

Cited by 3 publications

References 0 publications

Time-Frequency Transformation Technique with Various Mother Wavelets for DC Fault Analysis in HVDC Transmission Systems

Time-Frequency Transformation Technique with Various Mother Wavelets for DC Fault Analysis in HVDC Transmission Systems

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Application of Time-Frequency Analysis with Selection of Suitable Mother Wavelets for Transient States of HVDC Transmission Lines

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