An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform

Wang, Yanan; Li, Jianqiang; Pei, Yan; Ma, Zerui; Jia, Yanhe; Wei, Yu-Chih

doi:10.1016/j.ipm.2022.102867

Cited by 7 publications

(1 citation statement)

References 52 publications

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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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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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A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis

Chen

Zhao

Zhang

et al. 2022

Int J Comput Intell Syst

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In recent years, due to the non-stationary behavior of data samples, modeling and forecasting the stock price has been challenging for the business community and researchers. In order to address these mentioned issues, enhanced machine learning algorithms can be employed to establish stock forecasting algorithms. Accordingly, introducing the idea of “decomposition and ensemble” and the theory of “granular computing”, a hybrid model in this paper is established by incorporating the complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), independent component analysis (ICA), particle swarm optimization (PSO), and long short-term memory (LSTM). First, aiming at reducing the complexity of the original data of stock price, the CEEMD approach decomposes the data into different intrinsic mode functions (IMFs). To alleviate the cumulative error of IMFs, SE is performed to restructure the IMFs. Second, the ICA technique separates IMFs, describing the internal foundation structure. Finally, the LSTM model is adopted for forecasting the stock price results, in which the LSTM hyperparameters are optimized by synchronously utilizing the PSO algorithm. The experimental results on four stock prices from China stock market reveal the accuracy and robustness of the established model from the aspect of statistical efficiency measures. In theory, a useful attempt is made by integrating the idea of “granular computing” with “decomposition and ensemble” to construct the forecasting model of non-stationary data. In practice, the research results will provide scientific reference for the business community and researchers.

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Frequency characteristics analysis of pipe-to-soil potential under metro stray current interference using continuous wavelet transform method

Dong,

Zhang,

Gan

et al. 2023

Construction and Building Materials

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An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform

Cited by 7 publications

References 52 publications

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

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

A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis

Frequency characteristics analysis of pipe-to-soil potential under metro stray current interference using continuous wavelet transform method

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