Power quality disturbances classification using rotation forest and multi‐resolution fast S‐transform with data compression in time domain

Huang, Nantian; Da, Wang; Lin, Lin; Cai, Guowei; Huang, Guilin; Du, Jiping; Zheng, Jian

doi:10.1049/iet-gtd.2018.5439

Cited by 41 publications

(17 citation statements)

References 31 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we will choose the two most important features according to the importance of the features to build a two-dimensional feature space. By visualizing decision boundaries, we can intuitively see the distribution of different patterns in the feature space and the classification effect of different classifiers such as SVM [29], GBDT [30], RF [31] and Adaboost [32].…”

Section: The Classifier Performancementioning

confidence: 99%

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

et al. 2020

View full text Add to dashboard Cite

A novel pattern recognition method based on Empirical Mode Decomposition (EMD) and extreme gradient boosting (XGBoost) is proposed to recognize the disturbance events in phase sensitive optical time-domain reflectometer (ϕ-OTDR) to reduce nuisance alarm rate (NAR) and improve real-time performance in this paper. Eleven typical eigenvectors are extracted from components obtained by EMD of the disturbance signals and XGBoost is selected as a classifier to identify different type of disturbance signals. Five kinds of disturbance events, including watering, knocking, climbing, pressing and false disturbance event, can be identified, effectively. Experimental results show that NAR is 4.10% and identification time is 0.093 s. The recognition accuracy for the five patterns is 97.96%, 95.90%, 91.10%, 94.84% and 99.69%, respectively. The effectiveness of the proposed method is evaluated by using confusion matrix and decision boundary visualization. Experimental results demonstrate that our proposed pattern recognition method based on XGBoost has better performance in recognition rate and recognition time than other commonly used methods, such as support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Adaptive Boosting (Adaboost). INDEX TERMS Phase-sensitive optical time-domain reflectometer (ϕ-OTDR), extreme gradient boosting (XGBoost), nuisance alarm rate (NAR), empirical mode decomposition (EMD), pattern recognition, decision boundary visualization.

show abstract

Section: The Classifier Performancementioning

confidence: 99%

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

et al. 2020

View full text Add to dashboard Cite

show abstract

“…meters, and the scale of the aggregated load is generally small. For residential distribution networks, the difference in residents' electricity consumption behavior will increase the fluctuation and complexity of the aggregated load, which will have a negative impact on power quality [7]. What's more, it will also make the aggregate residential load forecasting more difficult [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Huang

Wang

Sining³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network. INDEX TERMS Aggregated load forecasting, feature importance, load fluctuation, crow search algorithm, S-Dbw, random forest.

show abstract

“…Common methods include S-Transform (ST) [ 8 ], Wavelet Transform (WT) [ 9 ], Empirical Mode Decomposition (EMD) [ 10 ], Local Mean Decomposition (LMD) [ 11 ], and Variable Mode Decomposition (VMD) [ 12 ]. ST provides a large number of time–frequency features, but it has a large amount of computation and is easily affected by parameters such as the window width factor [ 13 ]. WT has a good ability of local feature expression, but it is difficult to select wavelet bases in practical application [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Huang

2020

Entropy

Self Cite

View full text Add to dashboard Cite

The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

show abstract

Power quality disturbances classification using rotation forest and multi‐resolution fast S‐transform with data compression in time domain

Cited by 41 publications

References 31 publications

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Contact Info

Product

Resources

About