Islanding detection method for microgrids based on CatBoost

Chen, Ran; Zhou, Li; Xiong, Chuanyu; Xu, Hanping; Zhang, Zhaoyang; He, Xuhui; Dong, Qingguo; Wang, Can

doi:10.3389/fenrg.2022.1016754

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Future power networks could benefit from a bufferedmicrogrid design, which would highlight the importance of smooth microgrid control [39]. Through the identification and implementation of effective islanding detection and control technologies, the study aimed to enhance the safety and dependability of future power grids [40], [41]. Table 1 displays the results of the research that has come before.…”

Section: Using Gradient Boosting Decision Trees (Gbdt-js)mentioning

confidence: 99%

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

Muhammad Umar Khan

2024

SJET

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The increasing complexity of contemporary power distribution networks and the necessity to incorporate renewable energy sources have led to the rise in popularity of hybrid power distribution systems. A significant challenge for such systems is the reliable identification of multi-converter synchronous islanding mode, wherein numerous converters operate in synchronous islanded situations. Using state-of-the-art artificial intelligence ensemble techniques, we introduce "Vigil AI Assemble," a novel approach to detecting multi-converter synchronous islanding modes in hybrid power distribution systems. The proposed method enhances the precision and robustness of islanding situation recognition by combining various AI algorithms, each with its own distinct properties. By combining several AI-based techniques, the ensemble method can analyze system properties, waveform data, and communication in real-time.

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Section: Using Gradient Boosting Decision Trees (Gbdt-js)mentioning

confidence: 99%

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

Muhammad Umar Khan

2024

SJET

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“…Unlike a typical decision tree, a fully symmetric tree ensures that internal nodes at the same depth employ identical features and feature thresholds for splitting. Consequently, a fully symmetric tree can be represented as a decision table with 2 d entries, where d represents the number of levels in the tree [34]. This balanced structure enhances processing speed and improves feature handling compared to a standard decision tree.…”

Section: Catboost Model Based On Optimisation Of Optuna Hyperparametersmentioning

confidence: 99%

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

Zhang,

Wang

2023

Applied Sciences

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In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection.

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ANFIS-based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) for optimal operation of multiple grid-connected microgrids

Odonkor,

Akumu,

Moses

2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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Islanding detection method for microgrids based on CatBoost

Cited by 6 publications

References 27 publications

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

ANFIS-based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) for optimal operation of multiple grid-connected microgrids

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