Feature selection of power system transient stability assessment based on random forest and recursive feature elimination

Zhang, Chun; Li, Yansong; Yu, Zhihong; Fang, Tian

doi:10.1109/appeec.2016.7779696

Cited by 47 publications

(33 citation statements)

References 6 publications

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“…The database generation is then usually done offline, given the extensive simulation cost to build it, while the application of the resulting model trained on the dataset can be done offline or online, depending on the application and the context. [15], [16], [19], [22], [27]- [29], [32], [34], [35], [37], [38], [41], [43], [45], [47], [49]- [52], [54]- [56], [63], [64], [67]- [73], [75], [76], [81]- [83], [85]- [88], [90], [92], [93], [98], [102], [105], [107], [113], [115]- [117] Voltage stability [26], [30], [39], [40], [42], [44], [46], [48], [53],…”

Section: A Database Buildingmentioning

confidence: 99%

“…Table II provides an overview of the main data pre-processing methods used for DSA and DSC discussed below. [34], [35], [37] Genetic algorithms [38], [39] Tree-based algorithms [40]- [43] Feature extraction PCA and variants [44]- [46] Fisher's linear discriminant [47] Shapelets for time series [48] Deep learning auto-encoders [49]- [52] Class imbalance Oversampling [24], [53], [54] Cost-sensitive learning [53], [55] Ensemble methods [41], [45], [56], [57] 1) Feature engineering: Given the large number of features necessary to fully describe the state of a power system and the need for fast algorithms, feature selection techniques are proposed in many papers. Too many features can lead to excessive training time and, if many features are not relevant, could decrease the performance of the learnt model.…”

Section: B Data Pre-processingmentioning

confidence: 99%

“…The problem is particularly important, given that unstable events must be detected to guarantee the reliability of the system. To overcome this issue, several solutions have been proposed in the literature, such as oversampling the minority class [24], [53], [54], undersampling the majority class, exploiting a cost-sensitive learning [53], [55] and using ensemble methods [41], [45], [56], [57].…”

Section: B Data Pre-processingmentioning

confidence: 99%

“…Another example is the use of bagging and, instead of sampling uniformly in the training set, sampling to obtain balanced subsets of training set [45]. Finally, it is possible to use an Adaboost ensemble method, which adapts itself to class imbalance [57], or a 'Weighted Random Forest', that gives more weight to the unstable observations when learning [41].…”

Section: B Data Pre-processingmentioning

confidence: 99%

“…In [94], the authors explore a trade-off between predictive accuracy and interpretability. To improve accuracy, ensembles of decision trees such as Adaboost [27], [97], XGboost [43] and Random Forests [40], [41], [98]- [101] are proposed. In [101], uncertain predictions are checked with time-domain simulations.…”

Section: Learning a Modelmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: A Database Buildingmentioning

confidence: 99%

Section: B Data Pre-processingmentioning

confidence: 99%

Section: B Data Pre-processingmentioning

confidence: 99%

Section: B Data Pre-processingmentioning

confidence: 99%

Section: Learning a Modelmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study

Harrou

Dairi

et al. 2022

Concurrency and Computation

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Summary Dust, or particulate matter (PM2.5), is among the most harmful pollutants negatively affecting human health. Predicting indoor PM2.5 concentrations is essential to achieve acceptable indoor air quality. This study aims to investigate data‐driven models to accurately predict PM 2.5 pollution. Notably, a comparative study has been conducted between twenty‐one machine learning and deep learning models to predict PM2.5 levels. Specifically, we investigate the performance of machine learning and deep learning models to predict ambient PM2.5 concentrations based on other ambient pollutants, including SO2$$ {}_2 $$, NO2$$ {}_2 $$, O3$$ {}_3 $$, CO, and PM10. Here, we applied Bayesian optimization to optimally tune hyperparameters of the Gaussian process regression with different kernels and ensemble learning models (i.e., boosted trees and bagged trees) and investigated their prediction performance. Furthermore, to further enhance the forecasting performance of the investigated models, dynamic information has been incorporated by introducing lagged measurements in the construction of the considered models. Results show a significant improvement in the prediction performance when considering dynamic information from past data. Moreover, three methods, namely, random forest (RF), decision tree, and extreme gradient boosting, are applied to assess variables contribution and revealed that lagged PM2.5 data contribute significantly to the prediction performance and enables the construction of parsimonious models. Hourly concentration levels of ambient air pollution from the air quality monitoring network located in Seoul are employed to verify the prediction effectiveness of the studied models. Six measurements of effectiveness are used for assessing the prediction quality. Results showed that deep learning models are more efficient than the other investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost). Also, the results showed that the bidirectional long short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU) networks produce higher performance than the investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost) and deep learning models (i.e., LSTM, GRU, and convolutional neural network).

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Evaluating the Effectiveness of Wrapper Feature Selection Methods with Artificial Neural Network Classifier for Diabetes Prediction

Fahmiin

Lim

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Feature selection of power system transient stability assessment based on random forest and recursive feature elimination

Cited by 47 publications

References 6 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study

Evaluating the Effectiveness of Wrapper Feature Selection Methods with Artificial Neural Network Classifier for Diabetes Prediction

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