XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System

Chen, Minghua; Liu, Qunying; Chen, Shu‐Heng; Liu, Yicen; Zhang, Changhua; Liu, Ruihua

doi:10.1109/access.2019.2893448

Cited by 162 publications

(62 citation statements)

References 24 publications

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“…are employed as evaluation indicators to select the key features from the candidate features. To determine whether the information among the selected features is redundant, the Spearman correlation coefficient [54] is utilized to analyse the correlation between every two features. Spearman's correlation coefficient is a statistical measure of the strength of a monotonic relationship between paired data.…”

Section: ) Training Of the Xgboost-based Lcd Modelmentioning

confidence: 99%

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Han

2020

IEEE Access

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Lane-changing (LC) is a critical task for autonomous driving, especially in complex dynamic environments. Numerous automatic LC algorithms have been proposed. This topic, however, has not been sufficiently addressed in existing on-road manoeuvre decision methods. Therefore, this paper presents a novel LC decision (LCD) model that gives autonomous vehicles the ability to make human-like decisions. This method combines a deep autoencoder (DAE) network with the XGBoost algorithm. First, a DAE is utilized to build a robust multivariate reconstruction model using time series data from multiple sensors; then, the reconstruction error of the DAE trained with normal data is analysed for LC identification (LCI) and training data extraction. Then, to address the multi-parametric and nonlinear problem of the autonomous LC decision-making process, an XGBoost algorithm with Bayesian parameter optimization is adopted. Meanwhile, to fully train our learning model with large-scale datasets, we proposed an online training strategy that updates the model parameters with data batches. The experimental results illustrate that the DAE-based LCI model is able to accurately identify the LC behaviour of vehicles. Furthermore, with the same input features, the proposed XGBoost-based LCD model achieves better performance than other popular approaches. Moreover, a simulation experiment is performed to verify the effectiveness of the decision model. INDEX TERMS Autonomous vehicle, lane-changing identification, lane-changing decision-making, deep autoencoder network, XGBoost.

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Section: ) Training Of the Xgboost-based Lcd Modelmentioning

confidence: 99%

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Han

2020

IEEE Access

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“…XGBoost is one of the most efficient implementations of gradient boosted decision trees and it has been selected as one of the best offline machine learning algorithms used in Kaggle competitions [7], [29]. Specifically designed to optimize memory usage and exploit the hardware computing power, XGBoost decreases the execution time with an increased performance compared to many machine learning algorithms.…”

Section: B Offline Learning: Extreme Gradient Boosting Algorithmmentioning

confidence: 99%

A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement

et al. 2020

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The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade. Nowadays, there exist insurance products for most high-value assets such as vehicles, jewellery, health/life, and homes. Insurance companies are at the forefront in adopting cutting-edge operations, processes, and mathematical models to maximize profit whilst servicing their customers claims. Traditional methods that are exclusively based on human-in-theloop models are very time-consuming and inaccurate. In this paper, we develop a secure and automated insurance system framework that reduces human interaction, secures the insurance activities, alerts and informs about risky customers, detects fraudulent claims, and reduces monetary loss for the insurance sector. After presenting the blockchain-based framework to enable secure transactions and data sharing among different interacting agents within the insurance network, we propose to employ the extreme gradient boosting (XGBoost) machine learning algorithm for the aforementioned insurance services and compare its performances with those of other state-of-the-art algorithms. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. Furthermore, we propose an online learning solution to automatically deal with real-time updates of the insurance network and we show that it outperforms another online state-of-the-art algorithm. Finally, we combine the developed machine learning modules with the hyperledger fabric composer to implement and emulate the artificial intelligence and blockchain-based framework.

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“…Based on the power angle information of generators after fault clearance, and with reference to other researchers' experience in feature selection [25]- [27], 27 trajectory cluster features are constructed in this paper. The detailed description and calculation formulae are shown in Table 8 of Appendix.…”

Section: Transient Stability Assessment a Transient Stability Pmentioning

confidence: 99%

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

et al. 2020

IEEE Access

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In order to maintain the stable operation of power systems, quick and accurate transient stability assessment (TSA) after the fault clearance is important. Machine learning methods have been widely used in the transient stability analysis of power systems. However, how to make good use of time series data of PMUs and effectively balance the contradiction between rapidity and accuracy brings new challenges to TSA. To address this problem, we propose an anti-jitter dynamic evaluation method based on long-short term memory (LSTM) network. In this model, the trajectory cluster characteristics of generators power angles after fault clearance are taken as inputs, and an improved LSTM is used to learn the nonlinear mapping relationship between the input characteristics and the transient stability. Meanwhile, by the use of sliding time windows and anti-jitter mechanism, a hierarchical real-time prediction framework is constructed to effectively utilize the time series data of PMUs. The case studies on two systems indicate that the proposed method has superior evaluation accuracy and general performance. In addition, the proposed method can effectively evaluate the stability margin or instability degree of samples, which provides reliable reference information for emergency control. INDEX TERMS Deep learning, long-short term memory, recurrent neural network, transient stability assessment, artificial intelligence.

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XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System

Cited by 162 publications

References 24 publications

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

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